Data Analytics Lifecycle

INTRODUCTION TO BIG DATA ANALYTICS
1
. aspx, that displays the page shown as (2) in Figure 1-6. Arrivi ng at this site, the user may decide to click
to learn more about the process of becoming certified in data science. The user chooses a link to ward the
top of the page on Certifications, bringing the user to a new URL: ht tps : I I education. erne. com/
guest / certifica tion / framewo rk / stf / data_science . aspx, which is (3) in Figure 1-6.
Visiting these three websites adds three URLs to the log files monitoring the user’s computer or network
use. These three URLs are:
https: // www.google . com/# q=EMC+data+ s cience
https: // education . emc.com/ guest / campaign/ data science . aspx
https : // education . emc . com/ guest / certification/ framework / stf / data_
science . aspx
– – …… – .._.. …………. _
O.Uk*-andi’IO..~T~ · OIC~ o
—·- t…_ ·– . — ·-A– ——·—– .. -,.. _ , _____ ….
0.. ldHIWI • DtC (Ot.aiiOI\. l….,… and~ 0 — -~-~· 1 .. ……. _ .. _….._. __ , ___ -~-·-·
· ~—-“‘ .. ~_.,.. ….. –
:c ~::…~ and Cenbbcrt 0
t-e •·,-‘””””… ‘•’-“”‘•• ..,…._ _ … –……
~ …. __ …. …..,.,_…. … ,…._~·
–
~O•Uik~R…….., A0.1t-~~_,…h”, • £MC O ——–.. … .- . ‘”” …_. ______ , ______ …., –
– ···-.. … -~–.– ….
https:/ /www.google.com/#q
3
——
—
,_ __
—-
~-:::.::.::·–===-=-== .. ——·———·——..—::=–…..::..-..=-.:.-.=-…….
— ·——·—
-·—·–·—·~–·-· ———–·–·–., ______ … ___ ____ _ -·———-·-______ , _______ _
– ——-~ · –· —–
>l __ _ __ , , _ _ _
… , ——., :::… ::
FiGURE 1-6 Example of EMC Data Science search results

1.1 Big Data Overview
FIGURE 1-7 Example of unstructured data: video about Antarctica expedition [3]
This set of three URLs reflects the websites and actions taken to find Data Science inform ation related
to EMC. Together, this comprises a clicksrream that can be parsed and mined by data scientists to discover
usage patterns and uncover relation ships among clicks and areas of interest on a website or group of sites.
The four data types described in this chapter are sometimes generalized into two groups: structured
and unstructu red data. Big Data describes new kinds of data with which most organizations may not be
used to working. With this in mind, the next section discusses common technology arch itectures from the
standpoint of someone wanting to analyze Big Data.
1.1.2 Analyst Perspective on Data Repositories
The introduction of spreadsheets enabled business users to crea te simple logic on data structured in rows
and columns and create their own analyses of business problems. Database administrator training is not
requ ired to create spreadsheets: They can be set up to do many things qu ickly and independently of
information technology (IT) groups. Spreadsheets are easy to share, and end users have control over the
logic involved. However, their proliferation can result in “many versions of the t ruth.” In other words, it
can be challenging to determine if a particular user has the most relevant version of a spreadsheet, with
the most current data and logic in it. Moreover, if a laptop is lost or a file becomes corrupted, the data and
logic within the spreadsheet could be lost. This is an ongoing challenge because spreadsheet programs
such as Microsoft Excel still run on many computers worldwide. With the proliferation of data islands (or
spread marts), the need to centralize the data is more pressing than ever.
As data needs grew, so did mo re scalable data warehousing solutions. These technologies enabled
data to be managed centrally, providing benefits of security, failover, and a single repository where users

INTRODUCTION TO BIG DATA ANALYTICS
could rely on getting an “official” source of data for finan cial reporting or other mission-critical tasks. This
structure also enabled the creation ofOLAP cubes and 81 analytical tools, which provided quick access to a
set of dimensions within an RD8MS. More advanced features enabled performance of in-depth analytical
techniques such as regressions and neural networks. Enterprise Data Warehouses (EDWs) are critica l for
reporting and 81 tasks and solve many of the problems that proliferating spreadsheets introduce, such as
which of multiple versions of a spreadsheet is correct. EDWs-and a good 81 strategy-provide direct data
feeds from sources that are centrally managed, backed up, and secured.
Despite the benefits of EDWs and 81, these systems tend to restri ct the flexibility needed to perform
robust or exploratory data analysis. With the EDW model, data is managed and controlled by IT groups
and database administrators (D8As), and data analysts must depend on IT for access and changes to the
data schemas. This imposes longer lead ti mes for analysts to get data; most of the time is spent waiting for
approvals rather than starting meaningful work. Additionally, many times the EDW rul es restrict analysts
from building datasets. Consequently, it is com mon for additional systems to emerge containing critical
data for constructing analytic data sets, managed locally by power users. IT groups generally dislike exis-
tence of data sources outside of their control because, unlike an EDW, these data sets are not managed,
secured, or backed up. From an analyst perspective, EDW and 81 solve problems related to data accuracy
and availabi lity. However, EDW and 81 introduce new problems related to flexibility and agil ity, which were
less pronounced when dealing with spreads heets.
A solution to this problem is the analytic sandbox, which attempts to resolve the conflict for analysts and
data scientists with EDW and more formally managed corporate data. In this model, the IT group may still
manage the analytic sandboxes, but they will be purposefully designed to enable robust analytics, while
being centrally managed and secured. These sandboxes, often referred to as workspaces, are designed to
enable teams to explore many datasets in a controlled fashion and are not typically used for enterprise-
level financial reporting and sales dashboards.
Many times, analytic sa ndboxes enable high-performance computing using in-database processing-
the analytics occur within the database itself. The idea is that performance of the analysis will be better if
the analytics are run in the database itself, rather than bringing the data to an analytical tool that resides
somewhere else. In-database analytics, discussed further in Chapter 11, “Advanced Analytics- Technology
and Tools: In-Database Analytics.” creates relationships to multiple data sources within an organization and
saves time spent creating these data feeds on an individual basis. In-database processing for deep analytics
enables faster turnaround time for developing and executing new analytic models, while reducing, though
not eli minating, the cost associated with data stored in local, “shadow” file systems. In addition, rather
than the typical structured data in the EDW, analytic sandboxes ca n house a greater variety of data, such
as raw data, textual data, and other kinds of unstructured data, without interfering with critical production
databases. Table 1-1 summarizes the characteristics of the data repositories mentioned in this section.
TABLE 1-1 Types of Data Repositories, from an Analyst Perspective
Data Repository Characteristics
Spreadsheets and
data marts
(“spreadmarts”)
Spreadsheets and low-volume databases for record keeping
Analyst depends on data extracts.

Data Warehouses
Analytic Sandbox
(works paces)
1.2 State of the Practice in Analytics
Centralized data containers in a purpose-built space
Suppo rt s Bl and reporting, but restri cts robust analyses
Ana lyst d ependent o n IT and DBAs for data access and schema changes
Ana lysts must spend significant t ime to g et aggregat ed and d isaggre-
gated data extracts f rom multiple sources.
Data assets gathered f rom multiple sources and technologies fo r ana lysis
Enables fl exible, high-performance ana lysis in a nonproduction environ-
ment; can leverage in-d atabase processing
Reduces costs and risks associated w ith data replication into “shadow” file
systems
“Analyst owned” rather t han “DBA owned”
There are several things to consider with Big Data Analytics projects to ensure the approach fits w ith
the desired goals. Due to the characteristics of Big Data, these projects le nd them selves to decision su p-
port for high-value, strategic decision making w ith high processing complexi t y. The analytic techniques
used in this context need to be iterative and fl exible, due to the high volume of data and its complexity.
Performing rapid and complex analysis requires high throughput network con nections and a consideration
for the acceptable amount of late ncy. For instance, developing a real- t ime product recommender for a
website imposes greater syst em demands than developing a near· real·time recommender, which may
still pro vide acceptable p erform ance, have sl ight ly greater latency, and may be cheaper to deploy. These
considerations requi re a different approach to thinking about analytics challenges, which will be explored
further in the next section.
1.2 State of the Practice in Analytics
Current business problems provide many opportunities for organizations to become more analytical and
data dri ven, as shown in Table 1 ·2.
TABLE 1-2 Business Drivers for Advanced Analytics
Business Driver Examples
Optimize business operations
Identify business ri sk
Predict new business opportunities
Comply w ith laws or regu latory
requirements
Sales, pricing, profitability, efficiency
Customer churn, fraud, default
Upsell, cross-sell, best new customer prospects
Anti-Money Laundering, Fa ir Lending, Basel II-III, Sarbanes-
Oxley(SOX)

INTRODUCTION TO BIG DATA ANALYTICS
Table 1-2 outlines four categories of common business problems that organizations contend with where
they have an opportunity to leverage advanced analytics to create competitive advantage. Rather than only
performing standard reporting on these areas, organizations can apply advanced analytical techniques
to optimize processes and derive more value from these common tasks. The first three examples do not
represent new problems. Organizations have been trying to reduce customer churn, increase sales, and
cross-sell customers for many years. What is new is the opportunity to fuse advanced analytical techniques
with Big Data to produce more impactful analyses for these traditional problems. The last example por-
trays emerging regulatory requirements. Many compliance and regulatory laws have been in existence for
decades, but additional requirements are added every year, which represent additional complexity and
data requirements for organizations. Laws related to anti-money laundering (AML) and fraud prevention
require advanced analytical techniques to comply with and manage properly.
1.2.1 81 Versus Data Science
The four business drivers shown in Table 1-2 require a variety of analytical techniques to address them prop-
erly. Although much is written generally about analytics, it is important to distinguish between Bland Data
Science. As shown in Figure 1-8, there are several ways to compare these groups of analytical techniques.
One way to evaluate the type of analysis being performed is to examine the time horizon and the kind
of analytical approaches being used. Bl tends to provide reports, dashboards, and queries on business
questions for the current period or in the past. Bl systems make it easy to answer questions related to
quarter-to-date revenue, progress toward quarterly targets, and understand how much of a given product
was sold in a prior quarter or year. These questions tend to be closed-ended and explain current or past
behavior, typically by aggregating historical data and grouping it in some way. 81 provides hindsight and
some insight and generally answers questions related to “when” and “where” events occurred.
By comparison, Data Science tends to use disaggregated data in a more forward-looking, exploratory
way, focusing on analyzing the present and enabling informed decisions about the future. Rather than
aggregating historical data to look at how many of a given product sold in the previous quarter, a team
may employ Data Science techniques such as time series analysis, further discussed in Chapter 8, “Advanced
Analytical Theory and Methods: Time Series Analysis,” to forecast future product sales and revenue more
accurately than extending a simple trend line. In addition, Data Science tends to be more exploratory in
nature and may use scenario optimization to deal with more open-ended questions. This approach provides
insight into current activity and foresight into future events, while generally focusing on questions related
to “how” and “why” events occur.
Where 81 problems tend to require highly structured data organized in rows and columns for accurate
reporting, Data Science projects tend to use many types of data sources, including large or unconventional
datasets. Depending on an organization’s goals, it may choose to embark on a 81 project if it is doing reporting,
creating dashboards, or performing simple visualizations, or it may choose Data Science projects if it needs
to do a more sophisticated analysis with disaggregated or varied datasets.

Exploratory
Analytical
Approach
Explanatory
I
, .. — —,
1 Busin ess 1
1 Inte lligence 1
\ , …. _____ ..,
Past
fiGUR E 1 ·8 Comparing 81 with Data Science
1.2.2 Current Analytical Architecture
1 .2 State ofthe Practice In Analytlcs
Predictive Analytics and Data Mini ng
(Data Sci ence)
Typical • Optimization. predictive modo lin£
Techniques forocastlnC. statlatlcal analysis
and • Structured/unstructured data. many
Data Types types of sources, very Ioree datasata
Common
Questions
Typical
Techniques
and
Data Types
Tim e
Common
Questions
• What II … ?
• What’s tho optlmaltconarlo tor our bualnoss?
• What wtll happen next? What II these trend$
continuo? Why Is this happonlnt?
Busi ness Intelligence
• Standard and ad hoc reportlnc. dashboards.
alerts, queries, details on demand
• Structured data. traditional sourcoa.
manac:eable datasets
• What happened lut quarter?
• How many units sold?
• Whore Is the problem? In whic h situations?
Future
As described earlier, Data Science projects need workspaces that are purpose-built for experimenting with
data, with flexible and agile data architectures. Most organizations still have data warehouses that provide
excellent support for traditional reporting and simple data analysis activities but unfortunately have a more
difficult time supporting more robust analyses. This section examines a typical analytical data architecture
that may exist within an organization.
Figure 1-9 shows a typical data architecture and several of the challenges it presents to data scientists
and others trying to do advanced analytics. This section examines the data flow to the Data Scientist and
how this individual tits into the process of getting data to analyze on proj ects.

INTRODUCTION TO BIG DATA ANALYTICS
FIGURE 1-9 Typical analytic architecture
i..l ,_,
It
An alysts
Dashboards
Reports
Al erts
1. For data sources to be loaded into the data wa rehouse, data needs to be well understood,
structured, and normalized with the appropriate data type defini t ions. Although th is kind of
centralization enabl es security, backup, and fai lover of highly critical data, it also means that data
typically must go through significant preprocessing and checkpoints before it can enter this sort
of controll ed environment, which does not lend itself to data exploration and iterative analytic s.
2. As a result of t his level of control on the EDW, add itional local systems may emerge in the form of
departmental wa rehou ses and loca l data marts t hat business users create to accommodate thei r
need for flexible analysis. These local data marts may not have the same constraints for secu-
ri ty and structu re as the main EDW and allow users to do some level of more in-depth analysis.
However, these one-off systems reside in isolation, often are not synchronized or integrated with
other data stores, and may not be backed up.
3. Once in the data warehouse, data is read by additional applications across the enterprise for Bl
and reporting purposes. These are high-priority operational processes getting critical data feeds
from the data warehouses and repositories.
4. At the end of this workfl ow, analysts get data provisioned for their downstream ana lytics.
Because users generally are not allowed to run custom or intensive analytics on production
databases, analysts create data extracts from the EDW to analyze data offline in R or other local
analytical tools. Many times the se tools are lim ited to in-memory analytics on desktops analyz-
ing sa mples of data, rath er than the entire population of a dataset. Because the se analyses are
based on data extracts, they reside in a separate location, and the results of the analysis-and
any insights on the quality of the data or anomalies- rarely are fed back into the main data
repository.
Because new data sources slowly accum ulate in the EDW due to the rigorous validation and
data struct uring process, data is slow to move into the EDW, and the data schema is slow to change.

1.2 State of the Practice in Analytics
Departmental data warehouses may have been originally designed for a specific purpose and set of business
needs, but over time evolved to house more and more data, some of which may be forced into existing
schemas to enable Bland the creation of OLAP cubes for analysis and reporting. Although the EDW achieves
the objective of reporting and sometimes the creation of dashboards, EDWs generally limit the ability of
analysts to iterate on the data in a separate nonproduction environment where they can conduct in-depth
analytics or perform analysis on unstructured data.
The typical data architectures just described are designed for storing and processing mission-critical
data, supporting enterprise applications, and enabling corporate reporting activities. Although reports and
dashboards are still important for organizations, most traditional data architectures inhibit data exploration
and more sophisticated analysis. Moreover, traditional data architectures have several additional implica-
tions for data scientists.
o High-value data is hard to reach and leverage, and predictive analytics and data mining activities
are last in line for data. Because the EDWs are designed for central data management and reporting,
those wanting data for analysis are generally prioritized after operational processes.
o Data moves in batches from EDW to local analytical tools. This workflow means that data scientists
are limited to performing in-memory analytics (such as with R, SAS, SPSS, or Excel), which will restrict
the size of the data sets they can use. As such, analysis may be subject to constraints of sampling,
which can skew model accuracy.
o Data Science projects will remain isolated and ad hoc, rather than centrally managed. The implica-
tion of this isolation is that the organization can never harness the power of advanced analytics in a
scalable way, and Data Science projects will exist as nonstandard initiatives, which are frequently not
aligned with corporate business goals or strategy.
All these symptoms of the traditional data architecture result in a slow “time-to-insight” and lower
business impact than could be achieved if the data were more readily accessible and supported by an envi-
ronment that promoted advanced analytics. As stated earlier, one solution to this problem is to introduce
analytic sandboxes to enable data scientists to perform advanced analytics in a controlled and sanctioned
way. Meanwhile, the current Data Warehousing solutions continue offering reporting and Bl services to
support management and mission-critical operations.
1.2.3 Drivers of Big Data
To better understand the market drivers related to Big Data, it is helpful to first understand some past
history of data stores and the kinds of repositories and tools to manage these data stores.
As shown in Figure 1-10, in the 1990s the volume of information was often measured in terabytes.
Most organizations analyzed structured data in rows and columns and used relational databases and data
warehouses to manage large stores of enterprise information. The following decade saw a proliferation of
different kinds of data sources-mainly productivity and publishing tools such as content management
repositories and networked attached storage systems-to manage this kind of information, and the data
began to increase in size and started to be measured at petabyte scales. In the 2010s, the information that
organizations try to manage has broadened to include many other kinds of data. In this era, everyone
and everything is leaving a digital footprint. Figure 1-10 shows a summary perspective on sources of Big
Data generated by new applications and the scale and growth rate of the data. These applications, which
generate data volumes that can be measured in exabyte scale, provide opportunities for new analytics and
driving new value for organizations. The data now comes from multiple sources, such as these:

INTRODUCTION TO BIG DATA ANALYTICS
• Medical information, such as genomic sequencing and diag nostic imagi ng
• Photos and video footage uploaded to the World Wide Web
• Video surveillance, such as the thousands of video ca meras spread across a city
• Mobile devices, which provide geospatiallocation data of the users, as well as metadata about text
messages, phone calls, and application usage on smart phones
• Smart devices, which provide sensor-based collection of information from smart electric grids, smart
bu ildings, and many other public and ind ustry infrastructures
• Nontraditional IT devices, including the use of radio-freq uency identifica tion (RFID) reader s, GPS
navigation systems, and seismic processing
MEASURED IN MEASURED IN WILL BE MEASURED IN
TERABYTES PET A BYTES EXABYTES
lTB • 1.000GB lPB • l .OOOTB lEB l .OOOPB
IIEII
You(D
…. ~ .. ·,
A n ” \ . ~
I b ~
~
~
SMS
w: ‘—–”
ORACLE =
1.9905 20005 201.05
( RDBMS & DATA (CONTENT & DIGITAL ASSET (NO-SQL & KEY VALUE)
WAREHOUSE) MANAGEMENT)
FIGURE 1-10 Data evolution and the rise of Big Data sources
Th e Big Data t rend is ge nerating an enorm ous amount of information from many new sources. This
data deluge requires advanced analytics and new market players to take adva ntage of these opportunities
and new market dynamics, which wi ll be discussed in the following section.
1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics
Organ izations and data collectors are realizing that the data they ca n gath er from individuals contain s
intrinsic value and, as a result, a new economy is emerging. As this new digital economy continues to

1.2 State of the Practice in Analytics
evol ve, the market sees the introduction of data vendors and data cl eaners that use crowdsourcing (such
as Mechanica l Turk and Ga laxyZoo) to test the outcomes of machine learning techniques. Other vendors
offer added va lue by repackaging open source tools in a simpler way and bri nging the tools to market.
Vendors such as Cloudera, Hortonworks, and Pivotal have provid ed thi s value-add for the open source
framework Hadoop.
As the new ecosystem takes shape, there are four main groups of playe rs within this interconnected
web. These are shown in Figure 1-11.
• Data devices [shown in the (1) section of Figure 1-1 1] and the “Sensornet” gat her data from multiple
locations and continuously generate new data about th is data. For each gigabyte of new data cre-
ated, an additional petabyte of data is created about that data. [2)
• For example, consider someone playing an online video game through a PC, game console,
or smartphone. In this case, the video game provider captures data about the skill and levels
attained by the playe r. Intelligent systems monitor and log how and when the user plays the
game. As a consequence, the game provider can fine -tune the difficulty of the game,
suggest other related games that would most likely interest the user, and offer add itional
equipment and enhancements for the character based on the user’s age, gender, and
interests. Th is information may get stored loca lly or uploaded to the game provider’s cloud
to analyze t he gaming habits and opportunities for ups ell and cross-sell, and identify
archetypica l profiles of specific kinds of users.
• Smartphones provide another rich source of data . In add ition to messag ing and basic phone
usage, they store and transmit data about Internet usage, SMS usage, and real-time location.
This metadata can be used for analyzing traffic patterns by sca nning the density of smart-
phones in locations to track the speed of cars or the relative traffi c congestion on busy
roads. In t his way, GPS devices in ca rs can give drivers real-time updates an d offer altern ative
routes to avoid traffic delays .
• Retail shopping loyalty cards record not just the amo unt an individual spends, but the loca-
tions of stores that person visits, the kind s of products purchased, the stores where goods
are purchased most ofte n, and the combinations of prod ucts purchased together. Collecting
this data provides insights into shopping and travel habits and the likelihood of successful
advertiseme nt targeting for certa in types of retail promotions.
• Data collectors [the blue ovals, identified as (2) within Figure 1-1 1] incl ude sa mple entities that
col lect data from the dev ice and users.
• Data resul ts from a cable TV provider tracking the shows a person wa tches, which TV
channels someone wi ll and will not pay for to watch on demand, and t he prices someone is
will ing to pay fo r premiu m TV content
• Retail stores tracking the path a customer takes through their store w hile pushing a shop-
ping cart with an RFID chip so they can gauge which products get the most foot traffic using
geospatial data co llected from t he RFID chips
• Data aggregators (the dark gray ovals in Figure 1-11, marked as (3)) make sense of the data co llected
from the various entities from the “Senso rN et” or the “Internet ofThings.” These org anizatio ns
compile data from the devices an d usage pattern s collected by government agencies, retail stores,

INT RODUCTION TO BIG DATA ANALYTIC S
and websites. ln turn, t hey can choose to transform and package the data as products to sell to list
brokers, who may want to generate marketing lists of people who may be good targets for specific ad
campaigns.
• Data users and buyers are denoted by (4) in Figu re 1-11. These groups directly benefit from t he data
collected and aggregated by others within the data value chain.
• Retai l ba nks, acting as a data buyer, may want to know which customers have the hig hest
likelihood to apply for a second mortgage or a home eq uity line of credit. To provide inpu t
for this analysis, retai l banks may purchase data from a data aggregator. This kind of data
may include demograp hic information about people living in specific locations; people who
appear to have a specific level of debt, yet still have solid credit scores (or other characteris-
tics such as paying bil ls on time and having savings accounts) that can be used to infer cred it
worthiness; and those who are sea rching the web for information about paying off debts or
doing home remodeling projects. Obtaining data from these various sources and aggrega-
tors will enable a more targeted marketing campaign, which would have been more chal-
lenging before Big Data due to the lack of information or high-performing technologies.
• Using technologies such as Hadoop to perform natural language processing on
unstructured, textual data from social media websites, users can gauge the reaction to
events such as presidential campaigns. People may, for example, want to determine public
sentiments toward a candidate by analyzing related blogs and online comments. Similarl y,
data users may want to track and prepare for natural disasters by identifying which areas
a hurricane affects fi rst and how it moves, based on which geographic areas are tweeting
about it or discussing it via social med ia.
r:t\ Data
\.::J Devices {‘[I t Ptto…r r.r…, l UC)(.K VlOLU l !\I ill UO\. AI”
(,.\MI
CfitUII CAfW CO\tPl!UR
RfAO(H
~ .~
Iff [) \llOfO MfOICAI
IMo\C’oi”G
Law
EniCHCefllefll
Data
Users/ Buyers
0
Media
FIGURE 1-11 Emerging Big Data ecosystem
Do live!)’
So Mea
‘I If,. [Ill AN [
Privato
Investigators
/ lawyors

1.3 Key Roles for the New Big Data Ecosyst e m
As il lustrated by this emerging Big Data ecosystem, the kinds of data and the related market dynamics
vary greatly. These data sets ca n include sensor data, text, structured datasets, and social med ia . With this
in mind, it is worth recall ing that these data sets will not work wel l within trad itional EDWs, which were
architected to streamline reporting and dashboards and be centrally managed.lnstead, Big Data problems
and projects require different approaches to succeed.
Analysts need to partner with IT and DBAs to get the data they need within an analytic sandbox. A
typical analytical sandbox contains raw data, agg regated data, and data with mu ltiple kinds of structure.
The sandbox enables robust exploration of data and requires a savvy user to leverage and take advantage
of data in the sandbox environment.
1.3 Key Roles for the New Big Data Ecosystem
As explained in the context of the Big Data ecosystem in Section 1.2.4, new players have emerged to curate,
store, produce, clean, and transact data. In addition, the need for applying more advanced ana lytica l tech-
niques to increasing ly complex business problems has driven the emergence of new roles, new technology
platforms, and new analytical methods. This section explores the new roles that address these needs, and
subsequent chapters explore some of the analytica l methods and technology platforms.
The Big Data ecosystem demands three ca tegories of roles, as shown in Figure 1-12. These roles were
described in the McKinsey Global study on Big Data, from May 2011 [1].
Three Key Roles of The New Data Ecosystem
Role
Deep Analytical Talent
Data Savvy Professionals
Technology and Data Enablers
Data Scientists
.. Projected U.S. tal ent
gap: 1.40 ,000 to 1.90,000
.. Projected U.S. talent
gap: 1..5 million
Note: RcuresaboYe m~ • projected talent CDP In US In 201.8. as ihown In McKinsey May 2011 article “81& Data: l he Nut rront* t ot
Innovation. Competition. and Product~
FIGURE 1-12 Key roles of the new Big Data ecosystem
The first group- Deep Analytical Talent- is technically savvy, with strong analytical skills. Members pos-
sess a combi nation of skills to handle raw, unstructured data and to apply complex analytical techniques at

INTRODUCTION TO BIG DATA ANALYTICS
massive scales. This group has advanced training in quantitative disciplines, such as mathematics, statistics,
and machine learning. To do their jobs, members need access to a robust analytic sandbox or workspace
where they can perform large-scale analytical data experiments. Examples of current professions fitting
into this group include statisticians, economists, mathematicians, and the new role of the Data Scientist.
The McKinsey study forecasts that by the year 2018, the United States will have a talent gap of 140,000-
190,000 people with deep analytical talent. This does not represent the number of people needed with
deep analytical talent; rather, this range represents the difference between what will be available in the
workforce compared with what will be needed. In addition, these estimates only reflect forecasted talent
shortages in the United States; the number would be much larger on a global basis.
The second group-Data Savvy Professionals-has less technical depth but has a basic knowledge of
statistics or machine learning and can define key questions that can be answered using advanced analytics.
These people tend to have a base knowledge of working with data, or an appreciation for some of the work
being performed by data scientists and others with deep analytical talent. Examples of data savvy profes-
sionals include financial analysts, market research analysts, life scientists, operations managers, and business
and functional managers.
The McKinsey study forecasts the projected U.S. talent gap for this group to be 1.5 million people by
the year 2018. At a high level, this means for every Data Scientist profile needed, the gap will be ten times
as large for Data Savvy Professionals. Moving toward becoming a data savvy professional is a critical step
in broadening the perspective of managers, directors, and leaders, as this provides an idea of the kinds of
questions that can be solved with data.
The third category of people mentioned in the study is Technology and Data Enablers. This group
represents people providing technical expertise to support analytical projects, such as provisioning and
administrating analytical sandboxes, and managing large-scale data architectures that enable widespread
analytics within companies and other organizations. This role requires skills related to computer engineering,
programming, and database administration.
These three groups must work together closely to solve complex Big Data challenges. Most organizations
are familiar with people in the latter two groups mentioned, but the first group, Deep Analytical Talent,
tends to be the newest role for most and the least understood. For simplicity, this discussion focuses on
the emerging role of the Data Scientist. It describes the kinds of activities that role performs and provides
a more detailed view of the skills needed to fulfill that role.
There are three recurring sets of activities that data scientists perform:
o Reframe business challenges as analytics challenges. Specifically, this is a skill to diagnose busi-
ness problems, consider the core of a given problem, and determine which kinds of candidate analyt-
ical methods can be applied to solve it. This concept is explored further in Chapter 2, “Data Analytics
lifecycle.”
o Design, implement, and deploy statistical models and data mining techniques on Big Data. This
set of activities is mainly what people think about when they consider the role of the Data Scientist:

1.3 Key Roles for the New Big Data Ecosystem
namely, applying complex or advanced ana lytical methods to a variety of busi ness problems using
data. Chapter 3 t hrough Chapter 11 of this book introd uces the reader to many of the most popular
analytical techniques and tools in this area.
• Develop insights that lead to actionable recommendations. It is critical to note that applying
advanced methods to data problems does not necessarily drive new business va lue. Instead, it is
important to learn how to draw insights out of the data and communicate them effectively. Chapter 12,
“The Endgame, or Putting It All Together;’ has a brief overview of techniques for doing this.
Data scientists are generally thoug ht of as having fi ve mai n sets of skills and behaviora l characteristics,
as shown in Figure 1-13:
• Quantitative skill: such as mathematics or statistics
• Technical aptitude: namely, software engineering, machine learning, and programming skills
• Skeptical mind-set and critica l thin king: It is important that data scientists can examine their work
critica lly rather than in a one-sided way.
• Curious and creative: Data scientists are passionate about data and finding creative ways to solve
problems and portray information.
• Communicative and collaborative: Data scie ntists must be able to articulate the business val ue
in a clear way and collaboratively work with other groups, including project sponsors and key
stakeholders.
Quantitative
Technical
Skeptical
Curious and
Creative
Communlcativr
and
CDDaborati~
fiGURE 1 Profile of a Data Scientist

INTRODUCTION TO BIG DATA ANALYTICS
Data scientists are generally comfortable using this blend of skills to acquire, manage, analyze, and
visualize data and tell compelling stories about it. The next section includes examples of what Data Science
teams have created to drive new value or innovation with Big Data.
1.4 Examples of Big Data Analytics
After describing the emerging Big Data ecosystem and new roles needed to support its growth, this section
provides three examples of Big Data Analytics in different areas: retail, IT infrastructure, and social media.
As mentioned earlier, Big Data presents many opportunities to improve sa les and marketing ana lytics.
An example of this is the U.S. retailer Target. Cha rles Duhigg’s book The Power of Habit [4] discusses how
Target used Big Data and advanced analytical methods to drive new revenue. After analyzing consumer-
purchasing behavior, Target’s statisticians determin ed that the retailer made a great deal of money from
three main life-event situations.
• Marriage, when people tend to buy many new products
• Divorce, when people buy new products and change their spending habits
• Pregnancy, when people have many new things to buy and have an urgency to buy t hem
Target determined that the most lucrative of these life-events is the thi rd situation: pregnancy. Using
data collected from shoppers, Ta rget was able to identify this fac t and predict which of its shoppers were
pregnant. In one case, Target knew a female shopper was pregnant even before her family knew [5]. This
kind of knowledge allowed Target to offer specifi c coupons and incentives to thei r pregnant shoppers. In
fact, Target could not only determine if a shopper was pregnant, but in which month of pregnancy a shop-
per may be. This enabled Target to manage its inventory, knowi ng that there would be demand for specific
products and it wou ld likely vary by month over the com ing nine- to ten- month cycl es.
Hadoop [6] represents another example of Big Data innovation on the IT infra structure. Apache Hadoop
is an open source framework that allows companies to process vast amounts of information in a highly paral-
lelized way. Hadoop represents a specific implementation of t he MapReduce paradigm and was designed
by Doug Cutting and Mike Cafa rel la in 2005 to use data with varying structu res. It is an ideal technical
framework for many Big Data projects, which rely on large or unwieldy data set s with unconventiona l data
structures. One of the main benefits of Hadoop is that it employs a distributed file system, meaning it can
use a distributed cluster of servers and commodity hardware to process larg e amounts of data. Some of
the most co mmon examples of Hadoop imp lementations are in the social med ia space, where Hadoop
ca n manage transactions, give textual updates, and develop social graphs among millions of users. Twitter
and Facebook generate massive amounts of unstructured data and use Hadoop and its ecosystem of tools
to manage this hig h volu me. Hadoop and its ecosystem are covered in Chapter 10, “Adva nced Ana lytics-
Technology and Tools: MapReduce and Hadoop.”
Finally, social media represents a tremendous opportunity to leverage social and professional interac-
tions to derive new insights. Linked In exemplifies a company in which data itself is the product. Early on,
Linkedln founder Reid Hoffman saw the opportunity to create a social network for working professionals.

Exercises
As of 2014, Linkedln has more than 250 million user accounts and has added many additional features and
data-related products, such as recruiting, job seeker too ls, advertising, and lnMa ps, whic h show a social
graph of a user’s professional network. Figure 1-14 is an example of an In Map visualization that enables
a Linked In user to get a broader view of the interconnectedness of his contacts and understand how he
knows most of them .
fiGURE 1-14 Data visualization of a user’s social network using lnMaps
Summary
Big Data comes from myriad sources, including social media, sensors, the Internet ofThings, video surveil-
lance, and many sources of data that may not have been considered data even a few years ago. As businesses
struggle to keep up with changing market requirements, some companies are finding creative ways to apply
Big Data to their growing business needs and increasing ly complex problems. As organizations evolve
their processes and see the opportunities that Big Data can provide, they try to move beyond t raditional Bl
activities, such as using data to populate reports and dashboards, and move toward Data Science- driven
projects that attempt to answer more open-ended and complex questions.
However, exploiting the opportunities that Big Data presents requires new data architectures, includ –
ing analytic sandboxes, new ways of working, and people with new skill sets. These drivers are causing
organizations to set up analytic sandboxes and build Data Science teams. Although some organizations are
fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding
and hi ring data scientists in a timely man ner difficult. Still, organizations such as those in web retail, health
care, genomics, new IT infrast ructures, and social media are beginning to take advantage of Big Data and
apply it in creati ve and novel ways.
Exercises
1. What are the three characteristics of Big Data, and what are the main considerations in processing Big
Data?
2 . What is an analytic sa ndbox, and why is it important?
3. Explain the differences between Bland Data Science.
4 . Describe the challenges of the current analytical architecture for data scientists.
5. What are the key skill sets and behavioral characteristics of a data scientist?

INTRODUCTION TO BIG DATA ANALYTICS
Bibliography
[1] C. B. B. D. Manyika, “Big Data: The Next Frontier for Innovation, Competition, and Productivity,”
McKinsey Globa l Institute, 2011 .
[2] D. R. John Ga ntz, “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest
Growth in the Far East,” IDC, 2013.
[3] http: I l www. willisresilience . coml emc-data l ab [Online].
[4] C. Duhigg, The Power of Habit: Why We Do What We Do in Life and Business, New York: Random
House, 2012.
[5] K. Hil l, “How Target Figured Out a Teen Girl Was Pregnant Before Her Father Did,” Forb es, February
2012.
[6] http: I l hadoop. apache . org [Online].

DATA ANALYTICS LIFECYCLE
Data science projects differ from most traditional Business Intelligence projects and many data ana lysis
projects in that data science projects are more exploratory in nature. For t his reason, it is critical to have a
process to govern them and ensure t hat the participants are thorough and rigorous in their approach, yet
not so rigid that the process impedes exploration.
Many problems that appear huge and daunting at first can be broken down into smaller pieces or
actionable phases that can be more easily addressed. Having a good process ensures a comprehensive and
repeatable method for conducting analysis. In addition, it helps focus time and energy early in the process
to get a clear grasp of the business problem to be solved.
A common mistake made in data science projects is rushing into data collection and analysis, wh ich
precludes spending sufficient time to plan and scope the amount of work involved, understanding requ ire-
ments, or even framing the business problem properly. Consequently, participants may discover mid-stream
that the project sponsors are actually trying to achieve an objective that may not match the available data,
or they are attempting to address an interest that differs from what has been explicitly communicated.
When this happens, the project may need to revert to the initial phases of the process for a proper discovery
phase, or the project may be canceled.
Creating and documenting a process helps demonstrate rigor, which provides additional credibility
to the project when the data science team shares its findings. A well-defi ned process also offers a com-
mon framework for others to adopt, so the methods and analysis can be repeated in the future or as new
members join a team.
2.1 Data Analytics Lifecycle Overview
The Data Analytics Lifecycle is designed specifica lly for Big Data problems and data science projects. The
lifecycle has six phases, and project work can occur in several phases at once. For most phases in the life-
cycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended
to more closely portray a real project, in which aspects of the project move forward and may return to
earlier stages as new information is uncovered and team members learn more about various stages of the
project. This enables participants to move iteratively through the process and drive toward operationa l-
izing the project work.
2.1.1 Key Roles for a Successful Analytics Project
In recent years, substantial attention has been placed on the emerging role of the data scientist. In October
2012, Harvard Business Review featured an article titled “Data Scientist: The Sexiest Job of the 21st Century”
[1], in which experts OJ Patil and Tom Davenport described the new role and how to find and hire data
scientists. More and more conferences are held annually focusing on innovation in the areas of Data Science
and topics dealing with Big Data. Despite this strong focus on the emerg ing role of the data scientist specifi-
cally, there are actually seven key roles that need to be fulfilled for a high-functioning data science team
to execute analytic projects successfully.
Figure 2-1 depicts the various roles and key stakeholders of an analytics project. Each plays a critical part
in a successful ana lytics project. Although seven roles are listed, fewer or more peop le can accomplish the
work depending on t he scope of the project, the organizational structure, and the skills of t he participants.
For example, on a small, versatile team, these seven roles may be fulfilled by only 3 people, but a very large
proj ect may require 20 or more people. The seven roles follow.

2.1 Data Analytics Lifecycle Overview
…
•
FIGURE 2-1 Key roles for a successful analytics project
• Business User: Someone who understands the domain area and usually benefits from the resu lts.
Th is person can consult and advise the project team on the context of the project, the value of the
results, and how the outputs will be operationalized. Usually a business analyst, line manager, or
deep subject matter expert in the project domain fulfills this role.
• Project Sponsor: Responsible for the genesis of the project. Provides the impetus and requirements
for the project and defines the core business problem. Generally provides the funding and gauges
the degree of value from the final outputs of the working team. This person set s the priorities for the
project and clarifies the desired outputs.
• Proj ect Manage r: Ensures that key milestones and objectives are met on time and at the expected
quality.
• Busin ess Intelligence Analyst : Provides business domain expertise based on a deep understanding
of the data, key performance indicators (KPis), key metrics, and business intelligence from a reporting
perspective. Business Intelligence Analysts generally create dashboards and reports and have knowl-
edge of the data feeds and sources.
• Database Administrator (DBA): Provisions and configures the database environment to support
the analytics needs of the working team. These responsibilities may include provid ing access to
key databases or tables and ensuring the appropriate security levels are in place related to the data
repositories.
• Dat a Engineer: Leverag es deep technical skills to assist with tuning SQL queries for data manage-
ment and data extraction, and provides support for data ingestion into the analytic sandbox, which

DATA ANALYTICS LIFECYCLE
was discussed in Chapter 1, “Introduction to Big Data Analytics.” Whereas the DBA sets up and config-
ures the databases to be used, the data engineer executes the actual data extractions and performs
substantial data manipulation to facilitate the analytics. The data engineer works closely with the
data scientist to help shape data in the right ways for analyses.
o Data Scientist: Provides subject matter expertise for analytical techniques, data modeling, and
applying valid analytical techniques to given business problems. Ensures overall analytics objectives
are met. Designs and executes analytical methods and approaches with the data available to the
project.
Although most of these roles are not new, the last two roles-data engineer and data scientist-have
become popular and in high demand [2] as interest in Big Data has grown.
2.1.2 Background and Overview of Data Analytics Lifecycle
The Data Analytics Lifecycle defines analytics process best practices spanning discovery to project
completion. The lifecycle draws from established methods in the realm of data analytics and decision
science. This synthesis was developed after gathering input from data scientists and consulting estab-
lished approaches that provided input on pieces of the process. Several of the processes that were
consulted include these:
o Scientific method [3], in use for centuries, still provides a solid framework for thinking about and
deconstructing problems into their principal parts. One of the most valuable ideas of the scientific
method relates to forming hypotheses and finding ways to test ideas.
o CRISP-OM [4] provides useful input on ways to frame analytics problems and is a popular approach
for data mining.
o Tom Davenport’s DELTA framework [5]: The DELTA framework offers an approach for data analytics
projects, including the context of the organization’s skills, datasets, and leadership engagement.
o Doug Hubbard’s Applied Information Economics (AlE) approach [6]: AlE provides a framework for
measuring intangibles and provides guidance on developing decision models, calibrating expert
estimates, and deriving the expected value of information.
o “MAD Skills” by Cohen et al. [7] offers input for several of the techniques mentioned in Phases 2-4
that focus on model planning, execution, and key findings.
figure 2-2 presents an overview of the Data Analytics Lifecycle that includes six phases. Teams commonly
learn new things in a phase that cause them to go back and refine the work done in prior phases based
on new insights and information that have been uncovered. for this reason, figure 2-2 is shown as a cycle.
The circular arrows convey iterative movement between phases until the team members have sufficient
information to move to the next phase. The callouts include sample questions to ask to help guide whether
each of the team members has enough information and has made enough progress to move to the next
phase of the process. Note that these phases do not represent formal stage gates; rather, they serve as
criteria to help test whether it makes sense to stay in the current phase or move to the next.

Is t he model robust
enough? Have we
fai led for sure?
······ ::-.. ~~?
…….. ·\·
FIGURE 2-2 Overview of Data Analytics Lifecycle
2.1 Data Analytlcs Lifecycle Overview
Do I have enough
Information to draft
an analytic plan and
share for peer review?
Do I have
enough good
quality data to
start building
the model?
Do I have a good Idea
about the type of model
to try? Can I refine the
analytic plan?
Here is a brief overview of the main phases of the Data Analytics Lifecycle:
• Phase 1- Discovery: In Phase 1, the team learns the business domain, including relevant history
such as whether the organization or business unit has attempted similar projects in the past from
which they can learn. The team assesses the resources available to support the project in terms of
people, technology, time, and data. Important activities in this phase include fram ing the business
problem as an analytics challenge that can be addressed in subsequent phases and formulating ini-
tial hypotheses (IHs) to test and begin learn ing the data.
• Phase 2- Data prepa ration: Phase 2 requires the presence of an analytic sandbox, in which the
team can work with data and perform analytics for the duration of the project. The team needs to
execute ext ract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the
sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be t ransformed in the
ETLT process so t he team can work with it and analyze it. In t his phase, the team also needs to famil-
iarize itself with the data thoroughly and take steps to condition the data (Section 2.3.4).

DATA ANALYTICS LIFECYCLE
• Phase 3-Model planning: Phase 3 is model planning, where the team determines the methods,
techniques, and workflow it intends to follow for the subsequent model building phase. The team
explores the data to learn about the relationships between variables and subsequently se lects key
variables and the most suitable models.
• Phase 4-Mode l building: In Phase 4, the team deve lops data sets for testing, trai ning, and produc-
tion purposes. In addition, in this phase the team builds and executes models based on the work
done in the model planning phase. The team also considers whether its existing tools will suffice for
running the models, or if it will need a more robust environment for executing models and work flows
(for example, fast hardware and parallel processing, if applicable).
• Phase 5-Commu nicate results: In Phase 5, the team, in collaboration with major stakeholders,
determines if the results of the project are a success or a failure based on the criteria developed in
Phase 1. The team should identify key findings, quantify the business value, and develop a narrative
to summarize and convey findings to stakeholders.
• Phase 6-0perationalize: In Phase 6, the team delivers final reports, briefings, code, and technical
documents. In addition, the team may run a pilot project to implement the models in a production
envi ronment.
Once team members have run models and produced findings, it is critical to frame these results in a
way that is tailored to the audience that engaged the team . Moreover, it is critical to frame the results of
the work in a manner that demonstrates clear value. If the team performs a technically accurate analysis
but fails to translate the results into a language that resonates with the audience, people will not see the
value, and much of the time and effort on the project will have been wasted.
The rest of the chapter is organized as follows. Sections 2.2-2.7 discuss in detail how each of the six
phases works, and Section 2.8 shows a case study of incorporating the Data Analytics Lifecycle in a real-
world data science project.
2.2 Phase 1: Discovery
The first phase of the Data Analytics Lifecycle involves discovery (Figure 2-3).1n this phase, the data science
team must learn and investigate the problem, develop context and understanding, and learn about the
data sources needed and ava ilable for the project. In addition, the team formulates initial hypotheses that
can later be tested with data.
2.2.1 Learning the Business Domain
Understanding the domain area of the problem is essential. In many cases, data scientists will have deep
computational and quantitative knowledge that can be broadly applied across many disciplines. An example
of this role would be someone with an advanced degree in applied mathematics or statistics.
These data scientists have deep knowledge of the methods, technique s, and ways for applying heuris-
tics to a variety of business and conceptual problems. Others in this area may have deep knowledge of a
domain area, coupled with quantitative expertise. An example of this would be someone with a Ph.D. in
life sciences. This person would have deep knowledge of a field of study, such as oceanography, biology,
or genetics, with some depth of quantitative knowledge.
At this early stage in the process, the team needs to determine how much business or domain knowledge
the data scientist needs to develop models in Phases 3 and 4. The earlier the team can make this assessment

2.2 Phase 1: Discovery
the better, because t he decision helps dictate the resources needed for the project team and ensures the
tea m has t he right balance of domain knowledge and technica l expertise.
FIGURE 2-3 Discovery phase
2.2.2 Resources
Do I have enough
Inform ation to draft
an analytic plan and
share for peer review?
As part of t he discovery phase, the team needs to assess the resources ava ila ble to support the proj ect. In
this context, resources include technology, tools, system s, data, and people.
During this scoping, consider the available tools and technology t he team will be using and the types
of systems needed for later phases to operat ionalize the models. In add itio n, try to evaluate the level of
analytica l sophisticat ion within the orga nization and gaps that may exist related to tools, technology, and
skills. For instance, for th e model being developed to have longevity in an organization, consider what
types of skills and roles will be re qui red that may not exist today. For the proj ect to have long-term success,

DATA ANALYTIC$ LIFECVCLE
what types of skills and roles will be needed for the recipients of the model being developed? Does the
requisite level of expertise exist within the organization today, or will it need to be cultivated? Answering
these questions will influence the techniques the team selects and the kind of implementation the team
chooses to pursue in subsequent phases of the Data Analytics lifecycle.
In addition to the skills and computing resources, it is advisable to take inventory of the types of data
available to the team for the project. Consider if the data available is sufficient to support the project’s
goals. The team will need to determine whether it must collect additional data, purchase it from outside
sources, or transform existing data. Often, projects are started looking only at the data available. When
the data is less than hoped for, the size and scope of the project is reduced to work within the constraints
of the existing data.
An alternative approach is to consider the long-term goals of this kind of project, without being con-
strained by the current data. The team can then consider what data is needed to reach the long-term goals
and which pieces of this multistep journey can be achieved today with the existing data. Considering
longer-term goals along with short-term goals enables teams to pursue more ambitious projects and treat
a project as the first step of a more strategic initiative, rather than as a standalone initiative. It is critical
to view projects as part of a longer-term journey, especially if executing projects in an organization that
is new to Data Science and may not have embarked on the optimum datasets to support robust analyses
up to this point.
Ensure the project team has the right mix of domain experts, customers, analytic talent, and project
management to be effective. In addition, evaluate how much time is needed and if the team has the right
breadth and depth of skills.
After taking inventory of the tools, technology, data, and people, consider if the team has sufficient
resources to succeed on this project, or if additional resources are needed. Negotiating for resources at the
outset of the project, while seeping the goals, objectives, and feasibility, is generally more useful than later
in the process and ensures sufficient time to execute it properly. Project managers and key stakeholders have
better success negotiating for the right resources at this stage rather than later once the project is underway.
2.2.3 Framing the Problem
Framing the problem well is critical to the success of the project. Framing is the process of stating the
analytics problem to be solved. At this point, it is a best practice to write down the problem statement
and share it with the key stakeholders. Each team member may hear slightly different things related to
the needs and the problem and have somewhat different ideas of possible solutions. For these reasons, it
is crucial to state the analytics problem, as well as why and to whom it is important. Essentially, the team
needs to clearly articulate the current situation and its main challenges.
As part of this activity, it is important to identify the main objectives of the project, identify what needs
to be achieved in business terms, and identify what needs to be done to meet the needs. Additionally,
consider the objectives and the success criteria for the project. What is the team attempting to achieve by
doing the project, and what will be considered “good enough” as an outcome of the project? This is critical
to document and share with the project team and key stakeholders. It is best practice to share the statement
of goals and success criteria with the team and confirm alignment with the project sponsor’s expectations.
Perhaps equally important is to establish failure criteria. Most people doing projects prefer only to think
of the success criteria and what the conditions will look like when the participants are successful. However,
this is almost taking a best-case scenario approach, assuming that everything will proceed as planned

2.2 Phase 1: Discovery
and the project team will reach its goals. However, no matter how well planned, it is almost impossible to
plan for everything that will emerge in a project. The failure criteria will guide the team in understanding
when it is best to stop trying or settle for the results that have been gleaned from the data. Many times
people will continue to perform analyses past the point when any meaningful insights can be drawn from
the data. Establishing criteria for both success and failure helps the participants avoid unproductive effort
and remain aligned with the project sponsors
2.2.4 Identifying Key Stakeholders
Another important step is to identify the key stakeholders and their interests in the project. During
these discussions, the team can identify the success criteria, key risks, and stakeholders, which should
include anyone who will benefit from the project or will be significantly impacted by the project. When
interviewing stakeholders, learn about the domain area and any relevant history from similar analytics
projects. For example, the team may identify the results each stakeholder wants from the project and the
criteria it will use to judge the success of the project.
Keep in mind that the analytics project is being initiated for a reason. It is critical to articulate the pain
points as clearly as possible to address them and be aware of areas to pursue or avoid as the team gets
further into the analytical process. Depending on the number of stakeholders and participants, the team
may consider outlining the type of activity and participation expected from each stakeholder and partici-
pant. This will set clear expectations with the participants and avoid delays later when, for example, the
team may feel it needs to wait for approval from someone who views himself as an adviser rather than an
approver of the work product.
2.2.5 Interviewing the Analytics Sponsor
The team should plan to collaborate with the stakeholders to clarify and frame the analytics problem. At the
outset, project sponsors may have a predetermined solution that may not necessarily realize the desired
outcome. In these cases, the team must use its knowledge and expertise to identify the true underlying
problem and appropriate solution.
For instance, suppose in the early phase of a project, the team is told to create a recommender system
for the business and that the way to do this is by speaking with three people and integrating the product
recommender into a legacy corporate system. Although this may be a valid approach, it is important to test
the assumptions and develop a clear understanding of the problem. The data science team typically may
have a more objective understanding of the problem set than the stakeholders, who may be suggesting
solutions to a given problem. Therefore, the team can probe deeper into the context and domain to clearly
define the problem and propose possible paths from the problem to a desired outcome. In essence, the
data science team can take a more objective approach, as the stakeholders may have developed biases
over time, based on their experience. Also, what may have been true in the past may no longer be a valid
working assumption. One possible way to circumvent this issue is for the project sponsor to focus on clearly
defining the requirements, while the other members of the data science team focus on the methods needed
to achieve the goals.
When interviewing the main stakeholders, the team needs to take time to thoroughly interview the
project sponsor, who tends to be the one funding the project or providing the high-level requirements.
This person understands the problem and usually has an idea of a potential working solution. It is critical

DATA ANALYTIC S LIFE CYCLE
to thoroughly understand t he sponsor’s perspective to guide the team in getting started on the proj ect.
Here are some ti ps for interviewing project sponsors:
• Prepare for the interview; draft questio ns, and review with coll eagues.
• Use open-ended questi ons; avoid asking lead ing questions.
• Probe for details and pose foll ow-up questions.
• Avoid filling every silence in t he co nversation; give the other person time to think.
• Let the sponsors express t hei r ideas and ask clarifying questions, such as “Why? Is that correct? Is t his
idea on target? Is there anything else?”
• Use active listening techniques; repeat back what was heard to make sure t he team heard it correctly,
or reframe what was sa id.
• Try to avoid expressing the team’s opinions, which can introduce bias; instead, focus on listening.
• Be mindfu l of the body language of the interviewers and sta keholders; use eye contact where appro-
priate, and be attentive.
• Mi nimize distractions.
• Document what t he team heard, and review it with the sponsors.
Following is a brief list of common questions that are helpful to ask during the discovery phase when
interviewi ng t he project sponsor. The responses wi ll begin to shape the scope of the projec t and give the
team an idea of the goals and objectives of the project.
• What busi ness problem is t he team trying to solve?
• What is t he desired outcome of the proj ect?
• What data sources are available?
• What industry issues may impact t he analysis?
• What timelines need to be considered?
• Who could provide insight into the project?
• Who has final decision-making authority on the project?
• How wi ll t he focus and scope of t he problem change if the following dimensions change:
• Time: Analyzing 1 year or 10 years’ worth of data?
• People: Assess impact of changes in resources on project timelin e.
• Risk: Conservative to aggressive
• Resources: None to unlimited (tools, technology, systems)
• Size and attributes of data: Includi ng internal and external data sou rces

2.2 Phase 1: Discovery
2.2.6 Developing Initial Hypotheses
Developing a set of IHs is a key facet of the discovery phase. This step involves forming ideas that the team
can test with data. Generally, it is best to come up with a few primary hypotheses to test and then be
creative about developing several more. These IHs form the basis of the analytical tests the team will use
in later phases and serve as the foundation for the findings in Phase 5. Hypothesis testing from a statisti-
cal perspective is covered in greater detail in Chapter 3, “Review of Basic Data Analytic Methods Using R.”
In this way, the team can compare its answers with the outcome of an experiment or test to generate
additional possible solutions to problems. As a result, the team will have a much richer set of observations
to choose from and more choices for agreeing upon the most impactful conclusions from a project.
Another part of this process involves gathering and assessing hypotheses from stakeholders and domain
experts who may have their own perspective on what the problem is, what the solution should be, and how
to arrive at a solution. These stakeholders would know the domain area well and can offer suggestions on
ideas to test as the team formulates hypotheses during this phase. The team will likely collect many ideas
that may illuminate the operating assumptions of the stakeholders. These ideas will also give the team
opportunities to expand the project scope into adjacent spaces where it makes sense or design experiments
in a meaningful way to address the most important interests of the stakeholders. As part of this exercise,
it can be useful to obtain and explore some initial data to inform discussions with stakeholders during the
hypothesis-forming stage.
2.2.7 Identifying Potential Data Sources
As part of the discovery phase, identify the kinds of data the team will need to solve the problem. Consider
the volume, type, and time span of the data needed to test the hypotheses. Ensure that the team can access
more than simply aggregated data. In most cases, the team will need the raw data to avoid introducing
bias for the downstream analysis. Recalling the characteristics of Big Data from Chapter 1, assess the main
characteristics of the data, with regard to its volume, variety, and velocity of change. A thorough diagno-
sis of the data situation will influence the kinds of tools and techniques to use in Phases 2-4 of the Data
Analytics lifecycle.ln addition, performing data exploration in this phase will help the team determine
the amount of data needed, such as the amount of historical data to pull from existing systems and the
data structure. Develop an idea of the scope of the data needed, and validate that idea with the domain
experts on the project.
The team should perform five main activities during this step of the discovery phase:
o Identify data sources: Make a list of candidate data sources the team may need to test the initial
hypotheses outlined in this phase. Make an inventory of the datasets currently available and those
that can be purchased or otherwise acquired for the tests the team wants to perform.
o Capture aggregate data sources: This is for previewing the data and providing high-level under-
standing. It enables the team to gain a quick overview of the data and perform further exploration on
specific areas. It also points the team to possible areas of interest within the data.
o Review the raw data: Obtain preliminary data from initial data feeds. Begin understanding the
interdependencies among the data attributes, and become familiar with the content of the data, its
quality, and its limitations.

DATA ANALYTICS LIFEC YCLE
• Evaluate the data structures and tools needed: The data type and structure dictate which tools the
team can use to analyze the data. This evaluation gets the team thinking about which technologies
may be good candidates for the project and how to start getting access to these tools.
• Scope the sort of data infrastructure needed for this type of problem: In addition to the tools
needed, the data influences the kind of infrastructure that ‘s required, such as disk storage and net-
work capacity.
Unlike many traditional stage-gate processes, in which the team can advance only when specific criteria
are met, the Data Ana lytics Lifecycle is intended to accommodate more ambiguity. This more closely reflects
how data science projects work in real-life situati ons. For each phase of the process, it is recomm ended
to pass certain checkpoints as a way of gauging whether the team is ready to move to t he next phase of
the Data Analytics Lifecycle.
The team can move to the next phase when it has enough information to draft an analytics plan and
share it for peer review. Although a peer review of the plan may not actually be required by the project,
creating t he plan is a good test of the team’s grasp of the busin ess problem and the tea m’s approach
to add ressing it. Creating the analytic plan also requires a clear understanding of the domain area, the
problem to be solved, and scoping of the data sources to be used. Developing success criteria early in the
project clarifies the problem definition and helps the team when it comes time to make choices about
the analytical methods being used in later phases.
2.3 Phase 2: Data Preparation
The second phase of the Data Analytics Lifecycle involves data preparation, which includes the steps to
explore, preprocess, and condition data prior to model ing and analysis. In this phase, the team needs to
create a robust environment in which it can explore the data that is separate from a production environment.
Usua lly, this is done by preparing an ana lytics sandbox. To get the data into the sandbox, the team needs to
perform ETLT, by a combination of extracting, transforming, and load ing data into the sandbox. Once the
data is in the sa ndbox, the team needs to learn about the data and become familiar with it. Understandi ng
the data in detail is critical to t he success of the proj ect. The team also must decide how to condition and
transform data to get it into a format to facilitate subsequent analys is. The tea m may perform data visua liza-
tions to help team members understand the data, including its trends, outliers, and relationships among
data variables. Each of these steps of the data preparation phase is discussed throughout this section.
Data preparation tends to be the most labor-intensive step in the analytics lifecycle.ln fact, it is common
for teams to spend at least SOo/o of a data science project’s time in this critical phase. if the team cannot obtain
enough data of sufficient quality, it may be unable to perform the subsequent steps in the lifecycle process.
Figu re 2-4 shows an overview of the Data Analytics Lifecycle for Phase 2. The data preparation phase is
generally the most iterative and the one that teams tend to undere stimate most often. This is because most
teams and leaders are anxious to begin analyzing the data, testing hypotheses, and getting answers to some
of the questions posed in Phase 1. Many tend to jump into Phase 3 or Phase 4 to begin rapid ly developing
models and algorithms without spendi ng the time to prepare the data for modeling. Consequently, teams
come to realize the data they are working with does not allow them to execute the models they want, and
they end up back in Phase 2 anyway.

Frc;uRE 2 Data preparation phase
2.3.1 Preparing the Analytic Sandbox
2.3 Phase 2: Data Preparation
Do I have
enough good
quality dat a to
start building
the model?
The firs t subphase of data preparation requires the team to obtain an analytic sandbox (also commonly
referred to as a wo rkspace), in which the tea m ca n explore the data without interfering with live produc-
tion databa ses. Consider an exa mple in which the team needs to work with a company’s fin ancial data.
The team should access a copy of the fin ancial data from the analytic sand box rather than interacting with
the product ion version of t he organization’s ma in database, because that will be tight ly controlled and
needed for fi nancial reporting.
When developi ng the analytic sandbox, it is a best practice to collect all kinds of data there, as tea m
mem bers need access to high volumes and varieties of data for a Big Data analytics project. This ca n include

DATA ANALYTICS LIFECYCLE
everything from summary-level aggregated data, structured data, raw data feeds, and unstructured text
data from call logs or web logs, depending on the kind of analysis the team plans to undertake.
This expansive approach for attracting data of all kind differs considerably from the approach advocated
by many information technology (IT) organizations. Many IT groups provide access to only a particular sub-
segment of the data for a specific purpose. Often, the mindset of the IT group is to provide the minimum
amount of data required to allow the team to achieve its objectives. Conversely, the data science team
wants access to everything. From its perspective, more data is better, as oftentimes data science projects
are a mixture of purpose-driven analyses and experimental approaches to test a variety of ideas. In this
context, it can be challenging for a data science team if it has to request access to each and every dataset
and attribute one at a time. Because of these differing views on data access and use, it is critical for the data
science team to collaborate with IT, make clear what it is trying to accomplish, and align goals.
During these discussions, the data science team needs to give IT a justification to develop an analyt-
ics sandbox, which is separate from the traditional IT-governed data warehouses within an organization.
Successfully and amicably balancing the needs of both the data science team and IT requires a positive
working relationship between multiple groups and data owners. The payoff is great. The analytic sandbox
enables organizations to undertake more ambitious data science projects and move beyond doing tradi-
tional data analysis and Business Intelligence to perform more robust and advanced predictive analytics.
Expect the sandbox to be large.lt may contain raw data, aggregated data, and other data types that are
less commonly used in organizations. Sandbox size can vary greatly depending on the project. A good rule
is to plan for the sandbox to be at least 5-10 times the size of the original data sets, partly because copies of
the data may be created that serve as specific tables or data stores for specific kinds of analysis in the project.
Although the concept of an analytics sandbox is relatively new, companies are making progress in this
area and are finding ways to offer sandboxes and workspaces where teams can access data sets and work
in a way that is acceptable to both the data science teams and the IT groups.
2.3.2 Performing ETLT
As the team looks to begin data transformations, make sure the analytics sandbox has ample bandwidth
and reliable network connections to the underlying data sources to enable uninterrupted read and write.
In ETL, users perform extract, transform, load processes to extract data from a datastore, perform data
transformations, and load the data back into the datastore. However, the analytic sandbox approach differs
slightly; it advocates extract, load, and then transform.ln this case, the data is extracted in its raw form and
loaded into the datastore, where analysts can choose to transform the data into a new state or leave it in
its original, raw condition. The reason for this approach is that there is significant value in preserving the
raw data and including it in the sandbox before any transformations take place.
For instance, consider an analysis for fraud detection on credit card usage. Many times, outliers in this
data population can represent higher-risk transactions that may be indicative of fraudulent credit card
activity. Using ETL, these outliers may be inadvertently filtered out or transformed and cleaned before being
loaded into the datastore.ln this case, the very data that would be needed to evaluate instances of fraudu-
lent activity would be inadvertently cleansed, preventing the kind of analysis that a team would want to do.
Following the ELT approach gives the team access to clean data to analyze after the data has been loaded
into the database and gives access to the data in its original form for finding hidden nuances in the data.
This approach is part of the reason that the analytic sandbox can quickly grow large. The team may want
clean data and aggregated data and may need to keep a copy of the original data to compare against or

2.3 Phase 2: Data Preparation
look for hidden patterns that may have existed in the data before the cleaning stage. This process can be
summarized as ETLT to reflect the fact that a team may choose to perform ETL in one case and ELT in another.
Depending on the size and number of the data sources, the team may need to consider how to paral-
lelize the movement of the datasets into the sandbox. For this purpose, moving large amounts of data is
sometimes referred to as Big ETL. The data movement can be parallelized by technologies such as Hadoop
or MapReduce, which will be explained in greater detail in Chapter 10, “Advanced Analytics-Technology
and Tools: MapReduce and Hadoop.” At this point, keep in mind that these technologies can be used to
perform parallel data ingest and introduce a huge number of files or datasets in parallel in a very short
period of time. Hadoop can be useful for data loading as well as for data analysis in subsequent phases.
Prior to moving the data into the analytic sandbox, determine the transformations that need to be
performed on the data. Part of this phase involves assessing data quality and structuring the data sets
properly so they can be used for robust analysis in subsequent phases. In addition, it is important to con-
sider which data the team will have access to and which new data attributes will need to be derived in the
data to enable analysis.
As part of the ETLT step, it is advisable to make an inventory of the data and compare the data currently
available with datasets the team needs. Performing this sort of gap analysis provides a framework for
understanding which datasets the team can take advantage of today and where the team needs to initiate
projects for data collection or access to new datasets currently unavailable. A component of this sub phase
involves extracting data from the available sources and determining data connections for raw data, online
transaction processing (OLTP) databases, online analytical processing (OLAP) cubes, or other data feeds.
Application programming interface (API) is an increasingly popular way to access a data source [8]. Many
websites and social network applications now provide APis that offer access to data to support a project
or supplement the datasets with which a team is working. For example, connecting to the Twitter API can
enable a team to download millions of tweets to perform a project for sentiment analysis on a product, a
company, or an idea. Much of the Twitter data is publicly available and can augment other data sets used
on the project.
2.3.3 Learning About the Data
A critical aspect of a data science project is to become familiar with the data itself. Spending time to learn the
nuances of the datasets provides context to understand what constitutes a reasonable value and expected
output versus what is a surprising finding. In addition, it is important to catalog the data sources that the
team has access to and identify additional data sources that the team can leverage but perhaps does not
have access to today. Some of the activities in this step may overlap with the initial investigation of the
datasets that occur in the discovery phase. Doing this activity accomplishes several goals.
o Clarifies the data that the data science team has access to at the start of the project
o Highlights gaps by identifying datasets within an organization that the team may find useful but may
not be accessible to the team today. As a consequence, this activity can trigger a project to begin
building relationships with the data owners and finding ways to share data in appropriate ways. In
addition, this activity may provide an impetus to begin collecting new data that benefits the organi-
zation or a specific long-term project.
o Identifies datasets outside the organization that may be useful to obtain, through open APis, data
sharing, or purchasing data to supplement already existing datasets

DATA ANALYTICS LIFECYCLE
Table 2-1 demonstrates one way to organize this type of data inventory.
TABLE: 1 Sample Dataset Inventory
Data
Available Data to Obtain
and Data Available, but Data to from Third
Dataset Accessible not Accessible Collect Party Sources
Products shipped e
Product Fina ncials •
Product Ca ll Center •
Data
Live Product
Feedback Surveys
Product Sentiment
from Social Media
2.3.4 Data Conditioning
•
•
Data conditioning refers to the process of cleaning data, normalizing datasets, and perform ing trans-
formations on the data. A critical step with in the Data Ana lytics Lifecycle, data conditioning can involve
many complex steps to join or merge data sets or otherwise get datasets into a state that enables analysis
in further phases. Data conditioning is often viewed as a preprocessing step for the data analysis because
it involves many operations on the dataset before developing models to process or analyze the data. This
implies that the data-conditioning step is performed only by IT, the data owners, a DBA, or a data eng ineer.
However, it is also important to involve the data scientist in this step because many decisions are made in
the data conditioning phase that affect subsequent analysis. Part of this phase involves decidi ng which
aspects of particular datasets will be useful to analyze in later steps. Because teams begin forming ideas
in this phase about which data to keep and which data to transform or discard, it is important to involve
mu ltiple team members in these decisions. Leaving such decisions to a single person may cause teams to
return to this phase to retrieve data that may have been discarded.
As with the previous example of deciding which data to keep as it relates to fraud detection on credit
card usage, it is critical to be thoughtful about which data the team chooses to keep and which data will
be discarded. This can have far-reaching consequences that will cause the team to retrace previous steps
if th e team discards too much of the data at too early a point in this process. Typically, data science teams
would rather keep more data than too little data for the analysis. Additional questions and considerations
for the data conditioning step include these.
• What are the data sources? What are the target fields (for example, columns of the tables)?
• How clean is the data?

2.3 Phase 2: Data Preparation
o How consistent are the contents and files? Determine to what degree the data contains missing or
inconsistent values and ifthe data contains values deviating from normal.
o Assess the consistency of the data types. For instance, if the team expects certain data to be numeric,
confirm it is numeric or if it is a mixture of alphanumeric strings and text.
o Review the content of data columns or other inputs, and check to ensure they make sense. For
instance, if the project involves analyzing income levels, preview the data to confirm that the income
values are positive or if it is acceptable to have zeros or negative values.
o Look for any evidence of systematic error. Examples include data feeds from sensors or other data
sources breaking without anyone noticing, which causes invalid, incorrect, or missing data values. In
addition, review the data to gauge if the definition ofthe data is the same over all measurements. In
some cases, a data column is repurposed, or the column stops being populated, without this change
being annotated or without others being notified.
2.3.5 Survey and Visualize
After the team has collected and obtained at least some of the datasets needed for the subsequent
analysis, a useful step is to leverage data visualization tools to gain an overview of the data. Seeing high-level
patterns in the data enables one to understand characteristics about the data very quickly. One example
is using data visualization to examine data quality, such as whether the data contains many unexpected
values or other indicators of dirty data. (Dirty data will be discussed further in Chapter 3.) Another example
is skewness, such as if the majority of the data is heavily shifted toward one value or end of a continuum.
Shneiderman [9] is well known for his mantra for visual data analysis of “overview first, zoom and filter,
then details-on-demand.” This is a pragmatic approach to visual data analysis. It enables the user to find
areas of interest, zoom and filter to find more detailed information about a particular area of the data, and
then find the detailed data behind a particular area. This approach provides a high-level view of the data
and a great deal of information about a given dataset in a relatively short period of time.
When pursuing this approach with a data visualization tool or statistical package, the following guide-
lines and considerations are recommended.
o Review data to ensure that calculations remained consistent within columns or across tables for a
given data field. For instance, did customer lifetime value change at some point in the middle of data
collection? Or if working with financials, did the interest calculation change from simple to com-
pound at the end of the year?
o Does the data distribution stay consistent over all the data? If not, what kinds of actions should be
taken to address this problem?
o Assess the granularity of the data, the range of values, and the level of aggregation of the data.
o Does the data represent the population of interest? For marketing data, if the project is focused on
targeting customers of child-rearing age, does the data represent that, or is it full of senior citizens
and teenagers?
o For time-related variables, are the measurements daily, weekly, monthly? Is that good enough? Is
time measured in seconds everywhere? Or is it in milliseconds in some places? Determine the level of
granularity of the data needed for the analysis, and assess whether the current level of timestamps
on the data meets that need.

DATA ANALYTICS LIFECYCLE
• Is the data standardized/ normalized? Are the scales consistent? If not, how consistent or irregular is
the data?
• For geospatial datasets, are state or country abbreviations consistent across the data? Are personal
names normalized? English units? Metric units?
These are typical considerations that should be part of the thought process as the team evaluates the
data sets that are obtained for the project. Becoming deeply knowledgeable about the data will be critica l
when it comes time to construct and run models later in the process.
2.3.6 Common Tools for the Data Preparation Phase
Several tools are commonly used for this phase:
• Hadoop [10] ca n perform massively para llel ingest and custom analysis for web traffic parsing, GPS
location ana lytics, genomic analysis, and combining of massive unstructured data fe eds from mul-
tiple sources.
• Alpine Mi ner [11 ] provides a graphical user interface (GUI) for creating analytic work flows, includi ng
data manipu lations and a series of analytic events such as staged data-mining techniques (for exam-
ple, first select the top 100 customers, and then run descriptive statistics and clustering) on Postgres
SQL and other Big Data sources.
• Open Refine (formerly ca lled Google Refine) [12] is “a free, open source, powerful tool for working
with messy data.” It is a popular GUI-based tool for performing data transformations, and it’s one of
the most robust free tools cu rrentl y available.
• Simi lar to Open Refin e, Data Wrangler [13] is an interactive tool for data clean ing and transformation.
Wrangler was developed at Stanford University and can be used to perform many transformations on
a given dataset. In addition, data transformation outputs can be put into Java or Python. The advan-
tage of this feature is that a subset of the data can be manipulated in Wrangler via its GUI, and then
the same operations can be written out as Java or Python code to be executed against the full, larger
dataset offline in a local analytic sandbox.
For Phase 2, the team needs assistance from IT, DBAs, or whoever controls the Enterprise Data Warehouse
(EDW) for data sources the data science team would like to use.
2.4 Phase 3: Model Planning
In Phase 3, the data science team identifi es candidate models to apply to the data for clustering,
cla ssifying, or findin g relationships in the data depending on the goa l of the project, as shown in Fig ure 2-5.
It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became
acquainted with the data and understanding the business problems or domain area. These hypotheses help
the team fram e the analytics to execute in Phase 4 and select the right methods to achieve its objectives.
Some of the activities to consider in this phase include the following:
• Assess the structure of the datasets. The structure of the data sets is one factor that dictates the tools
and analytical techniques for the next phase. Depending on whether the team plans to analyze tex-
tual data or transactional data, for example, different tools and approaches are required.
• Ensure that the analytical techniques enable the team to meet the business objectives and accept or
reject the working hypotheses.

2 .4 Phase 3: Model Planning
• Determine if the situation warrants a single model or a series of techn iques as part of a larger ana lytic
workflow. A few example models include association rules (Chapter 5, “Advanced Ana lytical Theory
and Methods: Association Rules”) and logistic regression (Chapter 6, “Adva nced Analytical Theory
and Methods: Regression”). Other tools, such as Alpine Miner, enable users to set up a series of steps
and analyses and can serve as a front·end user interface (UI) for manipulating Big Data sources in
PostgreSQL.
FIGURE 2· 5 Model planning phase
Do I have a good Idea
about the type of model
to try? Can I refine the
analytic plan?
In addition to the considerations just listed, it is useful to research and understand how other ana lysts
generally approach a specific kind of problem. Given the kind of data and resources that are available, eva lu-
ate whether similar, existing approaches will work or if the team will need to create something new. Many
times teams can get ideas from analogous problems that other people have solved in different industry
verticals or domain areas. Table 2-2 summarizes the results of an exercise of this type, involving several
doma in areas and the types of models previously used in a classification type of problem after conducting
research on chu rn models in multiple industry verti ca ls. Performing this sort of diligence gives the team

DATAANALYT ICS LI FECYCLE
ideas of how others have solved similar problems and presents the team with a list of candidate models to
try as part of the model planning phase.
TABLE 2-2 Research on Model Planning in industry Verticals
Market Sector Analytic Techniques/Methods Used
Consumer Packaged
Goods
Retail Banking
Reta il Business
Wireless Telecom
Multiple linear regression, automatic relevance determination (ARD). and
decision tree
Multiple regression
Logistic regression, ARD, decision tree
Neural network, decision tree, hierarchical neurofuzzy systems, rule
evolver, logistic regression
2.4.1 Data Exploration and Variable Selection
Although some data exploration takes place in t he data preparation phase, those activities focus mainly on
data hygiene and on assessing the quality of the data itself. In Phase 3, the objective of the data exploration
is to understand the relationships among the variables to inform selection of the variables and methods
and to understand the prob lem domain. As with earlier phases of t he Data Analytics Lifecycle, it is impor-
tant to spend t ime and focus attention on this preparatory work to make t he subsequent phases of model
selection and execution easier and more efficient. A common way to cond uct this step involves using tools
to perform data visualizations. Approaching the data exploration in this way aids t he team in previewing
the data and assessing relationsh ips between varia bles at a high level.
In many cases, stakeholders and subject matter experts have instincts and hunches about what the data
science team should be considering and ana lyzing. Likely, this group had some hypothesis that led to the
genesis of the proj ect. Often, stakeholders have a good grasp of the problem and domain, although they
may not be aware of t he subtleties within the data or the model needed to accept or reject a hypothesi s.
Oth er times, sta keholders may be correct, bu t for the wrong reasons (for insta nce, they may be correct
about a correlation that exists but infer an incorrect reason for the corr elation). Meanwhile, data scientists
have to approach problems with an unbiased mind-set and be ready to question all assumptions.
As the team begi ns to question the incoming assumptions and test initial ideas of the projec t sponsors
and stakeholders, it needs to consider the inputs and data that will be needed, and then it must examine
whether these inputs are actually correlated with the outcomes that the team plans to predict or analyze.
Some methods and types of models will hand le correlated variables better than others. Depending on what
the team is attempting to solve, it may need to consider an alternate method, reduce the number of data
inputs, or transform the inputs to allow the team to use the best method for a given business problem.
Some of these techniques will be explored further in Chapter 3 and Chapter 6.
The key to this approach is to aim for capturing the most essential predictors and variables rather than
considering every possible variable that people think may influence the outcome. Approachi ng the prob-
lem in this manner requires iterations and testing to identify the most essential variables for the intended
analyses. The team should plan to test a range of variables to include in the model and then focus on the
most important and influential variab les.

2.4 Phase 3: Model Planning
If the team plans to run regression analyses, identify the candidate predictors and outcome variables
of the model. Plan to create variables that determine outcomes but demonstrate a strong relationship to
the outcome rather than to the other input variables. This includes remaining vigilant for problems such
as serial correlation, multicollinearity, and other typical data modeling challenges that interfere with the
validity of these models. Sometimes these issues can be avoided simply by looking at ways to reframe a
given problem. In addition, sometimes determining correlation is all that is needed (“black box prediction”),
and in other cases, the objective of the project is to understand the causal relationship better. In the latter
case, the team wants the model to have explanatory power and needs to forecast or stress test the model
under a variety of situations and with different datasets.
2.4.2 Model Selection
In the model selection subphase, the team’s main goal is to choose an analytical technique, or a short list
of candidate techniques, based on the end goal of the project. For the context of this book, a model is
discussed in general terms. In this case, a model simply refers to an abstraction from reality. One observes
events happening in a real-world situation or with live data and attempts to construct models that emulate
this behavior with a set of rules and conditions. In the case of machine learning and data mining, these
rules and conditions are grouped into several general sets of techniques, such as classification, association
rules, and clustering. When reviewing this list of types of potential models, the team can winnow down the
list to several viable models to try to address a given problem. More details on matching the right models
to common types of business problems are provided in Chapter 3 and Chapter 4, “Advanced Analytical
Theory and Methods: Clustering.”
An additional consideration in this area for dealing with Big Data involves determining if the team will
be using techniques that are best suited for structured data, unstructured data, or a hybrid approach. For
instance, the team can leverage MapReduce to analyze unstructured data, as highlighted in Chapter 10.
Lastly, the team should take care to identify and document the modeling assumptions it is making as it
chooses and constructs preliminary models.
Typically, teams create the initial models using a statistical software package such as R, SAS, or Matlab.
Although these tools are designed for data mining and machine learning algorithms, they may have limi-
tations when applying the models to very large datasets, as is common with Big Data. As such, the team
may consider redesigning these algorithms to run in the database itself during the pilot phase mentioned
in Phase 6.
The team can move to the model building phase once it has a good idea about the type of model to try
and the team has gained enough knowledge to refine the analytics plan. Advancing from this phase requires
a general methodology for the analytical model, a solid understanding of the variables and techniques to
use, and a description or diagram of the analytic workflow.
2.4.3 Common Tools for the Model Planning Phase
Many tools are available to assist in this phase. Here are several of the more common ones:
o R [14] has a complete set of modeling capabilities and provides a good environment for building
interpretive models with high-quality code.ln addition, it has the ability to interface with databases
via an ODBC connection and execute statistical tests and analyses against Big Data via an open
source connection. These two factors makeR well suited to performing statistical tests and analyt-
ics on Big Data. As of this writing, R contains nearly 5,000 packages for data analysis and graphical
representation. New packages are posted frequently, and many companies are providing value-add

D ATA ANALVTICS LIFECVCLE
services for R (such as train ing, instruction, and best practices), as well as packaging it in ways to make
it easier to use and more robust. This phenomenon is si milar to what happened with Linux in the late
1980s and ea rl y 1990s, when companies appeared to package and ma ke Linux easier for companies
to consume and deploy. UseR with fi le extracts for offline ana lysis and optimal performance, and use
RODBC connections for dynamic queri es and faster development.
• SQL Analysis services [1 5] ca n perform in-database analytics of common data mining fun ctions,
involved aggregations, and basic predicti ve models.
• SAS/ACCESS [16] provides integration bet ween SAS and the analytics sandbox via multiple data
con nectors such as OBDC, JOB(, and OLE DB. SAS itself is ge nera lly used on fil e extract s, but with
SAS/ACCESS, users ca n conn ect to relational databases (such as Orac le or Teradata) and data ware-
house appliances (such as Green plum or Aster), files, and enterpri se applications (such as SAP and
Sa lesforce.com).
2.5 Phase 4: Model Building
In Phase 4, the data science team needs to develop data sets for training, testing, and production purposes.
These data sets enable the data scientist to develop the analytical model and train it (“t rai ning data”), while
holding aside some of t he data (“hold-out data” or “test data”) for testing the model. (These topics are
addressed in more detail in Chapter 3.) During th is process, it is critical to ensure t hat the t raining and test
datasets are sufficiently robust for the model and analytical techn iques. A si mple way to t hink of these
datasets is to view the training dataset for cond ucting the initial experiments and the test sets for va lidating
an approach once the initia l experiments and models have been run.
In the model building phase, shown in Figure 2-6, an ana lytica l model is developed and fit on the trai n-
ing data and eva luated (scored) against t he test data. The phases of model planning and model building
can overl ap quite a bit, and in practice one ca n iterate back and forth between the two phases for a while
before settli ng on a final model.
Although the modeling techniques and logic required to develop models ca n be highly complex, the
actual dura tion of th is phase can be short compared to the time spent preparing the data and defi ning the
approaches. In general, plan to spend more ti me preparing and learning the data (Phases 1-2) and crafting
a pres entation of the fin di ngs (Phase 5). Phases 3 and 4 tend to move more quickly, although they are more
complex from a conceptual standpoint.
As part of this phase, t he data science tea m needs to execute the mod els defined in Phase 3.
During this phase, users run models from ana lytical software packages, such as R or SAS, on fil e extracts
and small data sets for testing purposes. On a small scale, assess t he va lidity of the model and its results.
For insta nce, determine if the model accounts for most of the data and has robust predictive power. At t his
point, refine the models to optimize the results, such as by modifying variable inputs or reducing correla ted
variables where appropriate. In Phase 3, the team may have had some knowledge of correlated variables
or problematic data attributes, w hich will be confirmed or denied once the models are actually executed.
When immersed in the details of constructing models and transforming data, many small decisions are often
made about the data and the approach for the modeli ng. These details can be easily forgotten once the
proj ect is completed. Therefore, it is vital to record t he resu lts and logic of the model du ring this phase. In
addi tion, one must take care to record any operating assumptions that were made in the modeling process
regarding the data or the context.

Is the model robust
enough? Have we
failed for sure?
FIGURE 2· 6 Model building phase
2.5 Phase 4 : Model Building
Creating robust models that are suitable to a specific situation requ ires thoughtful consideration to
ensure the models being developed ultimately meet the objectives outlined in Phase 1. Questions to con-
sider include these:
• Does the model appear valid and accurate on the test data?
• Does the model output/behavior make sense to the domain experts? That is, does it appear as if the
model is giving answers that make sense in this contex t?
• Do the parameter values of the fitted model make sense in the context of the domain?
• Is the model sufficiently accurate to meet the goal?
• Does the model avoid intolerable mistakes? Depending on context, false positives may be more seri-
ous or less serious than false negatives, for instance. (False positives and false negatives are discussed
further in Chapter 3 and Chapter 7, “Advanced Analytical Theory and Methods: Classification.”)

DATA ANALYT ICS LIFECYCLE
• Are more data or more inputs needed? Do any of the inputs need to be transformed or eliminated?
• Will the kind of model chosen support the runtime requirements?
• Is a different form of the model required to address the business problem? If so, go back to the model
planning phase and revise the modeling approach.
Once the data science team can evaluate either if the model is sufficiently robust to solve the problem
or if the team has failed, it can move to the next phase in the Data Analytics Lifecycle.
2.5.1 Common Tools for the Model Building Phase
There are many tools avai lable to assist in this phase, focused primarily on statistical analysis or data mining
soft wa re. Common tools in this space include, but are not limited to, the following:
• Commercial Tools:
• SAS Enterprise Mi ner (17) allows users to run predictive and descriptive models based on large
volumes of data from across the enterprise. It interoperates with other large data stores, has
many partnerships, and is built for enterpri se-level computing and analytics.
• SPSS Modeler [18) (provided by IBM and now called IBM SPSS Modeler) offers methods to
explore and analyze data through a GUI.
• Matlab [19) provides a high-level language for performing a variety of data analytics, algo-
rithms, and data exploration.
• Alpine Miner [1 1) provides a GUI front end for users to develop ana lytic workfiows and intera ct
with Big Data tools and platforms on the back end.
• STATISTICA [20) and Mathematica [21) are also popular and well-regarded data mining and
analytics tools.
• Free or Open Source tool s:
• Rand PL/R [14) R was described earlier in the model planning phase, and PL!R is a procedural
language for PostgreSQL with R. Using this approach means that R commands can be exe-
cuted in database. This technique provides higher performance and is more scalable than
running R in memory.
• Octave [22), a free software programming language for computational modeling, has some of
the functionality of Matlab. Because it is freely available, Octave is used in major universities
when teaching machine learning.
• WEKA [23) is a free data mining software package with an analytic workbench. The functions
created in WEKA can be executed within Java code.
• Python is a programming language that provides toolkits for machine learning and analysis,
such as scikit-learn, numpy, scipy, pandas, and related data visualization using matplotlib.
• SQL in-database implementations, such as MADlib [241. provide an alterative to in -memory
desktop analytical tools. MADiib provides an open-source machine learning library of algo-
rithms that can be executed in-database, for PostgreSQL or Greenplum.

2.6 Phase 5: Communicate Results
2.6 Phase 5: Communicate Results
After executing the model, the team needs to compare the outcomes of the modeling to the criteria estab-
lished for success and failure. In Phase 5, shown in Figure 2-7, the team considers how best to articulate
the findings and outcomes to the various team members and stakeholders, taking into account caveats,
assumptions, and any limita t ions of the results. Because the presentation is of ten circulated within an
orga nization, it is critical to articulate t he results properly and position the findings in a way that is appro-
priate for the audience.
FIGURE 2-7 Communicate results phase
. .. :.: ··· .. :~· ,.. .·
………. ~··
As part of Phase 5, the team needs to determine if it succeeded or failed in its objectives. Many times
people do not wa nt to admit to failing, but in this instance failure should not be considered as a true
failure, but rather as a failure of the data to accept or reject a given hypothesis adequately. This concept
can be counterintuitive for those w ho have been told their whole careers not to fail. However, t he key is

DATA ANALYTICS LIFEC YCLE
to remember that the team must be rigorous enough with the data to determine whether it wil l prove or
disprove the hypotheses outlined in Phase 1 (discovery). Sometimes teams have only done a superficial
analysis, which is not robust enough to accept or reject a hypothesis. Other times, teams perform very robust
analysis and are searching for ways to show results, even when results may not be there. It is important
to strike a balance between these two extremes when it comes to analyzing data and being pragmatic in
terms of showing real-world results.
When conducting this assessment, determine if the results are statistica lly significant and va lid. If they
are, identify the aspects of the results that stand out and may provide sa lient findings when it comes time
to communicate them. If the results are not valid, think about adjustments that can be made to refine and
iterate on the model to make it va lid. During this step, assess the re sults and identify which data points
may have been surprising and which were in line with the hypotheses that were developed in Phase 1.
Comparing the actual resu lts to the ideas formulated early on produces additiona l ideas and insights that
would have been missed if the team had not taken time to formulate initial hypotheses early in the process.
By this time, the team should have determined which model or models address the analytical challenge
in the most appropriate way. In addition, the team should have ideas of some of the findings as a result of the
project. The best practice in this phase is to record all the findings and then select the three most significant
ones that can be shared with the stakeholders. In addition, the team needs to reflect on the implications
of these findings and measure the business value. Depending on what emerged as a result of the model,
the team may need to spend time quantifying the business impact of the results to help prepare for the
presentation and demonstrate the value of the finding s. Doug Hubbard’s work [6) offers insights on how
to assess intangibles in business and quantify the value of seemingly unmeasurable th ings.
Now that the team has run the model, completed a thorough discovery phase, and learned a great deal
about the datasets, reflect on the project and consider what obstacles were in the project and what can be
improved in the future. Make recommendations for future work or improvements to existing processes, and
consider what each of the team members and sta keholders needs to fulfi ll her responsibil ities. For instance,
sponsors must champion the project. Stakeholders must understand how the model affects their processes.
(For example, if the team has created a model to predict customer churn, the Marketing team must under-
stand how to use the churn model predictions in planning their interventions.) Production eng ineers need
to operationalize the work that has been done. In addition, this is the phase to underscore the business
benefits of the work and begin making the case to implement the logic into a live production environment.
As a result of this phase, the team will have documented the key findings and major insights derived
from the analysis. The deliverable of this phase will be the most visible portion of the process to the outside
stakeholders and sponsors, so take care to clearly articulate the results, methodology, and business value
of the findings. More details will be provided about data visualization tools and references in Chapter 12,
“The Endgame, or Putting It All Together.”
2.7 Phase 6: Operationalize
In the final phase, the team communicates the benefits of the project more broadly and sets up a pilot
project to deploy the work in a controlled way before broadening the work to a full enterprise or ecosystem
of users. In Phase 4, the team scored the model in the analytics sandbox. Phase 6, shown in Figure 2-8,
represents the first time that most analytics teams approach dep loying the new ana lytical methods or
models in a production environment. Rather than deploying these models immediately on a wide-scale

2 .7 Phase 6 : Operationalize
basis, the risk can be managed more effectively and the team can learn by undertaking a small scope, pilot
deployment before a wide-scale rollout. This approach enables the team to learn about the performance
and related constraints of the model in a production environment on a small scale and make adjustments
before a full deployment. During the pilot project, the team may need to consider executing the algorithm
in the database rather than with in-memory tools such as R because the run time is significantly faster and
more efficient than running in-memory, especially on larger datasets.
FIGURE 2-8 Model operationalize phase
While scoping the effort involved in conducting a pilot project, consider running th e model in a
production environment for a discrete set of products or a single line of business, which tests the model
in a live setting. Thi s allows the team to learn from the deployment and make any needed adjustments
before launching the model across the enterprise. Be aware that this phase can bring in a new set of team
members- usually the engineers responsible for the production environment who have a new set of
issues and concerns beyond those of the core project team. This technical group needs to ensure that

DATA ANALYTIC$ LIFEC YCLE
running the model fits smoothly into the production environ ment and that the model can be integrated
into related business processes.
Part of the operationalizing phase includes creating a mechanism for performing ongoing monitoring
of model accu racy and, if accuracy degrades, finding ways to retrain the model. If fea sible, design alert s
for when the model is operating “out-of-bounds.” This includes situations when the inputs are beyond the
range that the model was trained on, which may cause the outputs of the model to be inaccurate or invalid.
If this begins to happen regularly, the model needs to be retrained on new data.
Often, analytical projects yield new insights about a business, a problem, or an idea that people may
have taken at face value or thought was impossible to explore. Four main deliverables can be created to
meet the needs of most stakeholders. This approach for developing the four deliverables is discussed in
greater detail in Chapter 12.
Figure 2-9 portrays the key outputs for each of the main stakeholders of an analytics project and what
they usually expect at the conclusion of a project.
• Busi ness Use r typically tries to determine the benefit s and implications of the findings to the
business.
• Proj ect Sponsor typica lly asks questions related to the business impact of the project, the risks and
return on investment (ROI ), and the way the project ca n be evangelized within the organization (and
beyond).
• Project Ma nager needs to determine if the project was completed on time and within budget and
how well the goals were met.
• Business Intellig ence Analyst needs to know if the reports and dashboards he manages will be
impacted and need to change.
• Data Engineer and Database Administrator (DBA) typical ly need to share their code from the ana-
lytics project and create a technical document on how to implement it.
• Dat a Scientist needs to share the code and explain the model to her peers, managers, and other
stakeholders.
Although these seven roles represent many interests within a project, these interests usually overlap,
and most of them can be met with four main deliverables.
• Presentation for project sponsors: This contains high-level takeaways for executive leve l stakehold-
ers, with a few key messages to aid th eir decision-making process. Focus on clean, easy visuals for the
prese nter to explain and for the viewer to grasp.
• Presentation for analysts, which describes business process changes and reporting changes. Fellow
data scientists will want the details and are comfortable with technical graphs (such as Receiver
Operating Characteristic [ROC) curves, density plots, and histograms shown in Chapter 3 and Chapter 7).
• Code for technical people.
• Technical specifications of implementing the code.
As a general rul e, the more executive the audience, the more succinct the presentation needs to be.
Most executive sponsors attend many briefin gs in the course of a day or a week. Ensure that the presenta-
tion gets to the point quickly and frames the results in terms of value to the sponsor’s organization. For
instance, if the team is working with a bank to analyze cases of credit card fraud, highlight the frequency
of fraud, the number of cases in the past month or year, and the cost or revenue impact to the bank

2.8 Case Study: Globa l Innovation Netw ork and Analysis (GINA)
(or focu s on the reverse-how much more revenue the bank could gain if it addresses the fraud problem).
This demonstrates the business impact better than deep dives on the methodology. The presentation
needs to include supporting information about analytical methodology and data sources, but genera lly
only as supporting detail or to ensure the audience has confidence in the approach that was taken to
analyze the data.
-Key Outputs from a Successful Analytic Project
Code Presentation for Analysts
== Technlt411 Specs 11:011 Present1t1on for Project Sponsors
•
FIGURE 2-9 Key outputs from a successful ana lyrics project
When presenting to other audiences with more quantitative backgrounds, focus more time on the
methodology and findings. In these in stances, the team can be more expansive in describing t he out-
comes, methodology, and analytical experiment with a peer group. This audience will be more interested
in the techniques, especia lly if the team developed a new way of processing or analyzing data that can be
reused in the future or appl ied to similar problems. In addition, use imagery or data visualization when
possible. Although it may take more time to develop imagery, people tend to remember mental pictu re s to
demonstrate a point more than long lists of bullets [25]. Data visualization and presentations are discussed
further in Chapter 12.
2.8 Case Study: Global Innovation Network
and Analysis (GINA)
EMC’s Global Innovation Network and Analytics (GINA) team is a group of senior technologists located in
centers of excellence (COEs) around the world. This team’s charter is to engage employees across global
COEs to drive innovation, research, and university partnerships. In 2012, a newly hired director wanted to

DATA ANALYTICS LIFECYCLE
improve these activities and provide a mechanism to track and analyze the related information. In addition,
this team wanted to create more robust mechanisms for capturing the results of its informal conversations
with other thought leaders within EMC, in academia, or in other organizations, which could later be mined
for insights.
The GINA team thought its approach would provide a means to share ideas globally and increase
knowledge sharing among GINA members who may be separated geographically. It planned to create a
data repository containing both structured and unstructured data to accomplish three main goals.
o Store formal and informal data.
o Track research from global technologists.
o Mine the data for patterns and insights to improve the team’s operations and strategy.
The GINA case study provides an example of how a team applied the Data Analytics Ufecycle to analyze
innovation data at EMC.Innovation is typically a difficult concept to measure, and this team wanted to look
for ways to use advanced analytical methods to identify key innovators within the company.
2.8.1 Phase 1: Discovery
In the GINA project’s discovery phase, the team began identifying data sources. Although GINA was a
group of technologists skilled in many different aspects of engineering, it had some data and ideas about
what it wanted to explore but lacked a formal team that could perform these analytics. After consulting
with various experts including Tom Davenport, a noted expert in analytics at Babson College, and Peter
Gloor, an expert in collective intelligence and creator of CoiN (Collaborative Innovation Networks) at MIT,
the team decided to crowdsource the work by seeking volunteers within EMC.
Here is a list of how the various roles on the working team were fulfilled.
o Business User, Project Sponsor, Project Manager: Vice President from Office of the CTO
o Business Intelligence Analyst: Representatives from IT
o Data Engineer and Database Administrator (DBA): Representatives from IT
o Data Scientist: Distinguished Engineer, who also developed the social graphs shown in the GINA
case study
The project sponsor’s approach was to leverage social media and blogging [26] to accelerate the col-
lection of innovation and research data worldwide and to motivate teams of “volunteer” data scientists
at worldwide locations. Given that he lacked a formal team, he needed to be resourceful about finding
people who were both capable and willing to volunteer their time to work on interesting problems. Data
scientists tend to be passionate about data, and the project sponsor was able to tap into this passion of
highly talented people to accomplish challenging work in a creative way.
The data for the project fell into two main categories. The first category represented five years of idea
submissions from EMC’s internal innovation contests, known as the Innovation Road map (formerly called the
Innovation Showcase). The Innovation Road map is a formal, organic innovation process whereby employees
from around the globe submit ideas that are then vetted and judged. The best ideas are selected for further
incubation. As a result, the data is a mix of structured data, such as idea counts, submission dates, inventor
names, and unstructured content, such as the textual descriptions of the ideas themselves.

2.8 Case Study: Global Innovation Network and Analysis (GINA)
The second category of data encompassed minutes and notes representing innovation and research
activity from around the world. This also represented a mix of structured and unstructured data. The
structured data included attributes such as dates, names, and geographic locations. The unstructured
documents contained the “who, what, when, and where” information that represents rich data about
knowledge growth and transfer within the company. This type of information is often stored in business
silos that have little to no visibility across disparate research teams.
The 10 main IHs that the GINA team developed were as follows:
o IH1: Innovation activity in different geographic regions can be mapped to corporate strategic
directions.
o IH2: The length oftime it takes to deliver ideas decreases when global knowledge transfer occurs as
part of the idea delivery process.
o IH3: Innovators who participate in global knowledge transfer deliver ideas more quickly than those
who do not.
o IH4: An idea submission can be analyzed and evaluated for the likelihood of receiving funding.
o IHS: Knowledge discovery and growth for a particular topic can be measured and compared across
geographic regions.
o IH6: Knowledge transfer activity can identify research-specific boundary spanners in disparate
regions.
o IH7: Strategic corporate themes can be mapped to geographic regions.
o IHS: Frequent knowledge expansion and transfer events reduce the time it takes to generate a corpo-
rate asset from an idea.
o IH9: Lineage maps can reveal when knowledge expansion and transfer did not (or has not) resulted in
a corporate asset.
o IH1 0: Emerging research topics can be classified and mapped to specific ideators, innovators, bound-
ary spanners, and assets.
The GINA (IHs) can be grouped into two categories:
o Descriptive analytics of what is currently happening to spark further creativity, collaboration, and
asset generation
o Predictive analytics to advise executive management of where it should be investing in the future
2.8.2 Phase 2: Data Preparation
The team partnered with its IT department to set up a new analytics sandbox to store and experiment on
the data. During the data exploration exercise, the data scientists and data engineers began to notice that
certain data needed conditioning and normalization. In addition, the team realized that several missing
data sets were critical to testing some of the analytic hypotheses.
As the team explored the data, it quickly realized that if it did not have data of sufficient quality or could
not get good quality data, it would not be able to perform the subsequent steps in the lifecycle process.
As a result, it was important to determine what level of data quality and cleanliness was sufficient for the

DATA ANALYTICS LIFECYCLE
project being undertaken. In the case of the GINA, the team discovered that many of the names of the
researchers and people interacting with the universities were misspelled or had leading and trailing spaces
in the datastore. Seemingly small problems such as these in the data had to be addressed in this phase to
enable better analysis and data aggregation in subsequent phases.
2.8.3 Phase 3: Model Planning
In the GINA project, for much of the dataset, it seemed feasible to use social network analysis techniques to
look at the networks of innovators within EMC.In other cases, it was difficult to come up with appropriate
ways to test hypotheses due to the lack of data. In one case (IH9), the team made a decision to initiate a
longitudinal study to begin tracking data points over time regarding people developing new intellectual
property. This data collection would enable the team to test the following two ideas in the future:
o IHS: Frequent knowledge expansion and transfer events reduce the amount oftime it takes to
generate a corporate asset from an idea.
o IH9: Lineage maps can reveal when knowledge expansion and transfer did not (or has not} result(ed)
in a corporate asset.
For the longitudinal study being proposed, the team needed to establish goal criteria for the study.
Specifically, it needed to determine the end goal of a successful idea that had traversed the entire journey.
The parameters related to the scope of the study included the following considerations:
o Identify the right milestones to achieve this goal.
o Trace how people move ideas from each milestone toward the goal.
o Once this is done, trace ideas that die, and trace others that reach the goal. Compare the journeys of
ideas that make it and those that do not.
o Compare the times and the outcomes using a few different methods (depending on how the data is
collected and assembled). These could be as simple as t-tests or perhaps involve different types of
classification algorithms.
2.8.4 Phase 4: Model Building
In Phase 4, the GINA team employed several analytical methods. This included work by the data scientist
using Natural Language Processing (NLP} techniques on the textual descriptions of the Innovation Road map
ideas. In addition, he conducted social network analysis using Rand RStudio, and then he developed social
graphs and visualizations of the network of communications related to innovation using R’s ggplot2
package. Examples of this work are shown in Figures 2-10 and 2-11.

2.8 Case Study: Global Innovation Network and Analysis (GINA)
• • , t-ft11lf f
·-·· ‘-..lr f .. fi”-CIW1o
FIGURE 2-10 Social graph [27] visualization of idea submitt ers and finalists
• •
•
0
0 0 0
0 o o o 0 0
0
0 oo
O o
0
()
o ocflr_ og
Betweenness Ranks 0 ..() 0
1. 578 o cr~
2. 5 11 0 0() 0
3. 341 0
4. 171 0

summary (sales)
# plot num_of_orders vs. sales
plot(sales$num_of_orders,sales$sales_total,
main .. “Number of Orders vs. Sales”)
# perform a statistical analysis (fit a linear regression model)
results <- lm(sales$sales_total - sales$num_of_orders) summary(results) # perform some diagnostics on the fitted model # plot histogram of the residuals hist(results$residuals, breaks .. 800) 3.1 Introduction toR In this example, the data file is imported using the read. csv () function. Once the file has been imported, it is useful to examine the contents to ensure that the data was loaded properly as well as to become familiar with the data. In the example, the head ( ) function, by default, displays the first six records of sales. # examine the imported dataset head(sales) cust id sales total num of orders gender - - - - 100001 800.64 2 100002 217.53 100003 74.58 2 t·l 4 100004 ·198. 60 t•l 5 100005 723.11 4 F 6 100006 69.43 2 F The summary () function provides some descriptive statistics, such as the mean and median, for each data column. Additionally, the minimum and maximum values as well as the 1st and 3rd quartiles are provided. Because the gender column contains two possible characters, an "F" (female) or "M" (male), the summary () function provides the count of each character's occurrence. summary(sales) cust id i"lin. :100001 1st Qu . : 1 o 2 5o 1 l>ledian :105001
!>lean :105001
3rd Qu. :107500
l\lax. : 110 0 0 0
sales total
!’-lin. 30.02
1 s t Qu . : 8 0 . 2 9
r’ledian : 151.65
t•lean 24 9. 46
3 rd Qu. : 2 9 5 . 50
t•lax. :7606.09
num of orde1·s gender
!’-lin. 1.000 F:5035
1st Qu.: 2.000 !·l: 4965
t>ledian : 2.000
!>lean 2.428
3rd Qu.: 3.000
1•1ax. :22.000
Plotting a dataset’s contents can provide information about the relationships between the vari-
ous columns. In this example, the plot () function generates a scatterplot of the number of orders
(sales$num_of_orders) againsttheannual sales (sales$sales_total). The$ is used to refer-
ence a specific column in the dataset sales. The resulting plot is shown in Figure 3-1.
# plot num_of_orders vs. sales
plot(sales$num_of_orders,sales$sales_total,
main .. “Number of Orders vs. Sales”)

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
Number of Orders vs . Total Sales
0
iii 0 0 0
:§ 0 0
I <0 C/) Q> 0
iii 0 § 0 0 C/) 0 0 II> 0
I I i i i
8 8 0 0 0 ~ C/) 0 0 Q> 0
• 8 iii N I I C/) • 0
5 10 15 20
sales$num_ of_ orders
FtGURE 3-1 Graphically examining th e data
Each point corresponds to the number of orders and the total sales for each customer. The plot indicates
that the annual sales are proportional to the number of orders placed. Although the observed relationship
between these two variables is not purely linear, the ana lyst decided to apply linear regression using the
lm () function as a first step in the model ing process.
r esul t s <- lm(sal es$sa l es_total - sales$num_of _o r de rs) r e s ults ca.l: lm formu.a sa.c Ssales ~ota. sales$num_of_orders Coefti 1en· In· er ep • sa:essnum o f orders The resulting intercept and slope values are -154.1 and 166.2, respectively, for the fitted linear equation. However, results stores considerably more information that can be examined with the summary () fun ction. Details on the contents of results are examined by applying the at t ributes () fun ction. Because regression analysis is presented in more detail later in the book, the reader shou ld not overly focus on interpreting the following output. summary(results) Call : lm formu:a sa!esSsales_total - salcs$ num_of_orders Re!'a ilnls: Min IQ Med1an 3C 1·1ax -666 . 5 12S . S - 26 . 7 86 . 6 4103 . 4 Coe f ficie nt:s: Est1mate Std . Errol r value Prl> t
Intercept -15~.128
sal~s$num f orders 166 . 22l
.; . 12″‘ – 37 . 33
1 . 462 112 . 66
<2e-16 <2e- ~6 3.1 Introduction to R Sior:1t . codes : 0 ' ... . 0.00! •·· · c . o: • • • 5 I • I . 1 I 1 Res1aua. star:da~d e~ro~ : ~! .: on 999° deg~ees of :reeo~m ~ultlple R·squar d : 0 . ~617 , Aa:usted P-sq~a~ed : . 561 The summary () function is an example of a generic function. A generic function is a group of fu nc- tions sharing the same name but behaving differently depending on the number and the type of arguments they receive. Utilized previously, plot () is another example of a generic function; the plot is determi ned by the passed variables. Generic functions are used throughout this chapter and t he book. In t he final portion of the example, the foll owing R code uses the generic function hist () to ge nerate a histogram (Figure 3-2) of t he re siduals stored in results. The function ca ll illustrates that optional parameter values can be passed. In this case, the number of breaks is specified to observe the large residua ls. ~ pert H. some d13gnosLics or. the htted m .. de. # plot hist >gnm f the residu, ls
his t (r esults $res idua l s, breaks= 8 00)
Histogra m of resultsSresid uals
0
I()
>-u
c 0 .. 0
:J
<:T ~ 0 u. I() 0 0 1000 2000 3000 resuttsSres1duals FIGURE 3-2 Evidence of large residuals 4000 This simple example illustrates a few of the basic model planning and bu ilding tasks that may occur in Phases 3 and 4 of the Data Analytics Lifecycl e. Throughout this chapter, it is useful to envision how the presented R fun ctionality will be used in a more comprehensive analysis. 3.1.1 R Graphical User Interfaces R software uses a command-line interface (CLI) that is similar to the BASH shell in Li nux or the interactive versions of scripting languages such as Python. UNIX and Linux users can enter command Rat the termina l prompt to use the CU. For Windows installations, R comes with RGui.exe, which provides a basic graphica l user interface (GU I). However, to im prove the ease of writing, executing, and debugging R code, several additional GUis have been written for R. Popular GUis include the R commander [3]. Ra ttle [4], and RStudio [5). This section presents a brief overview of RStudio, w hich was used to build the R examples in th is book. Figure 3-3 provides a screenshot of the previous R code example executed in RStudio. RE V IEW O F BASIC D ATA ANALYTIC M ETHODS USING R ._ CNtl. .. .. t 1 ules .. r t -.d.uv " ... tw..o u l n ' 1 • -...1 • n • • "' 6llu,-...,.1.,_uh,1.u.,· !~uln • - - ....._.. -....y .. tJ - ..... O....ft• f .:1- .-------, ulu 10000 OM. ol " " whbhs Scripts rn~o~hs ... .. , ... ... ... ... .•. ... ... ... plot u1ts~of-orcl9ors,u1es ules_tot•l, utn-·~ ~ cwo...s '"· s..ln . ' ruulu t"ltSIIIU •. ' to' 1 ' Jo flt I T 1• uln ,u lu_utul ulu~,..._ot_orct«'s 11 luu - .... ,..,.,... ""'- ; :- •toMtt· 0 { a. ..... j Workspace '" lU Ul •Ill r t f " ~ I hht ruuhs1t"tst~•h. br u11 • 100 r Histogram or resu lts$ reslduals ·-'" •• • iu~-~.~ ... ~------:=::::::::::::::::~ "s-uy(resulu) c•H: l•(f or-..1& - ulu lulu_uul .. saln~of_orcMf's) ... , .... ls : tUn tQ lllt'dhn 1Q ~b · 6M. 1 - US, \ •U .7 t6.6 .&10), <1 lsttuu Ud. Crrw 1 " ' ',.,. k(,.Jtl) omuupt) . ,,,, u, •.ut -Jr.n ~ •. ,, •·• ul•s~oLoro.-' u6. Ul 1. 41 61 uJ.M <-lt- 16 ••• sttnU . codts : o '• •• ' o. oot •••• 0.01 ••• o.os •, • 0.1 • • 1 l:utdlu• 1 sund¥d uror: 110.1 on tnt CS•vr"' or fr~ tty\ttph I:•S.,¥-.1: 0 , $6 17, .lodJw~t f'd l•squ.vl'd: 0 . )6)7 f" • Uathttc : t . ltl••Ool on I wrd 9Ht Of", P•VA1U41: ot l . h•16 ,. #pf'rfor• ~- diA~ltn Of' Uw ttlud _,.., Jo ?lot hhtOQrM ot tt,. re~lduh ~ Mn(ruwh~k'n lcN.th, .,.,,,, · tOO) FIGURE 3-3 RStudio GUI .:1 Console j The fou r highlighted window panes follow. ~ ~ j ~ ~ • Scripts: Serves as an area to write and saveR code 1000 • Workspace: Lists the datasets and variables in the R environment Plots 2000 3000 • Plot s: Displays the plots generated by the R code and provides a straightfor ward mechanism to export the plots • Co nsole: Provides a history of the executed R code and the output Additionally, the console pane can be used to obtain help information on R. Figure 3-4 illustrates that by entering ? lm at the console prompt, the help details of the lm ( ) function are provided on the right. Alternatively, help ( lm ) could have been entered at the console prompt. Functions such as edit () and fix () allow the user to update the contents of an R variable. Alternatively, such changes can be implemented with RStudio by selecting the appropriate variable from the workspace pan e. R allows one to save the work space environment, includ ing variables and loaded li brari es, into an . Rdata fil e using the save . image () function. An existing . Rdata file can be loaded using the load . image () function . Tools such as RS tud io prompt the user for whether the developer wants to save the workspace connects prior to exiting the GUI. The reader is encouraged to install Rand a preferred GUI to try out the R examples provided in the book and utilize the help functionality to access more details about the discussed topics. ., .. t tt • • ........... , 41 1ft f Ot l.a.l:?' ~~ t" u lu N r e...cl.csv 411\1 , • .,.1yJ•1n.u\ " ... ... . .. hud ulu s~salu . ,. ,. .. . ._. . "' .. , plot ultJ l......_.,....ot'MrS ,Uh.tSnles.toul. .. ,,..-~of arden'''~· ~·u· , .. >01 …
>07 , .. , ..
110
• f • .I H-It t II t>t .II It~ r–r;~l~ ~\)
ru.ulu 1• uluSulu,.ICKil ulus~of .. crd~
ru~ln
Ul “r•·f dl \1 nt,,d~l
U7 •ph•t hi t< t t~ '"' ••I lU hht ru11hs Srut duah, bru~s • 100 1 u• --- ,;.·,;·iah .... ~ •• ;;;.;::.:---------=========----' un: lo(f or-.la .. ultsSul•s .. tO'CAI .. uohsJtuil,..of_or~s ) luto.uh : Min IQ .. fltiM )Q '""' ~tM. , · US. S • lt.7 M , t 4110), 41 CCHff lc.l..r:ts: Uttaau St d. lrror t "' ' '"' "'(•I t I>
(fnurcept) -1~.UI 1000000″)
head(housing_data )

4552088
4 45″- 88
5 8699:!93 6
5
5
5
9
9
5
3.1 Intro ductio n t o R
Although plots can be saved using the RStudio GUI, plots can also be saved using R code by specifying
the appropriate g raphic devices. Using the j peg () function, the following R code creates a new JPEG
file, adds a histogram plot to the file, and then closes the fi le. Such techniques are useful w hen automating
standard repor ts. Other functions, such as png () , bmp () , pdf () ,and postscript () ,are available
in R to save plots in the des ired format.
jpeg ( fil e= “c : /data/ sale s_h ist . j peg” )
h ist(sales$num_of_ o rders )
creaLe a ne'” jpeg file
# export histogt·;un to jpeg
d ev. o ff () ~ shut off the graphic device
More information on data imports and exports can be fou nd at http : I I cran . r-proj e ct . o rgl
doc I ma nuals I r- rel ease i R- d a ta . html, such as how to import data sets from statistical software
packages including Minitab, SAS, and SPSS.
3.1.3 Attribute and Data Types
In the earli er exa mple, the sal es variable contained a record for each cu st omer. Several cha racteristic s,
such as total an nual sa les, number of orders, and gender, were provided for each customer. In general,
these characteristics or attributes provide the qualitative and quantitative measures for each item or subject
of interest. Attributes can be categorized into four types: nominal, ordinal, interval, and ratio (NOIR) [8).
Table 3-2 distinguishes these four attrib ute types and shows the operations they support. Nominal and
ordinal attributes are considered categorical attributes, w hereas interval and ratio attributes are considered
numeric attributes.
TABLE 3-2 NOIR Attribu te Types
Categorical (Qualitative) Numeric (Quantitative)
Nominal Ordina l Inte rv al Rat io
Definition The va lues represent Attributes The difference Both the difference
labels that distin- imply a betw een two and the ratio of
guish one from sequence. values is two values are
another. meaningful. meaningful.
Examples ZIP codes, nationa l- Quality of Temperature in Age, temperature
ity, street names, diamonds, Celsius or in Kelvin, counts,
gender, employee ID academic Fahrenheit, ca l- length, weight
numbers, TRUE or grades, mag- endar dates,
FALS E nitude of lati tudes
ea rthquakes
Operat ions =, >’ =, ~, = , ;t., =,~,
< , s , > , 2: <, s , > , c:, <, s , >, ~,
+, – + , – ,
x, .:-

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
Data of one attribute type may be converted to another. For example, the qual it yof diamonds {Fair,
Good, Very Good, Premium, Ideal} is considered ordinal but can be converted to nominal {Good, Excellent}
with a defined mapping. Similarly, a ratio attribute like Age can be converted into an ordinal attribute such
as {Infant, Adolescent, Adult, Senior}. Understanding the attribute types in a given dataset is important
to ensure that the appropriate descriptive statistics and analytic methods are applied and properly inter-
preted. For example, the mean and standard deviation of U.S. postal ZIP codes are not very meaningful or
appropriate. Proper handling of categorical variables will be addressed in subsequent chapters. Also, it is
useful to consider these attribute types during the following discussion on R data types.
Numeric, Character, and Logical Data Types
Like other programming languages, R supports the use of numeric, character, and logical (Boolean) values.
Examples of such variables are given in the following R code.
i <- 1 sport <- "football" flag <- TRUE # create a numeric variable # create a character variable # create a logical variable R provides several functions, such as class () and type of (),to examine the characteristics of a given variable. The class () function represents the abstract class of an object. The typeof () func- tion determines the way an object is stored in memory. Although i appears to be an integer, i is internally stored using double precision. To improve the readability of the code segments in this section, the inline R comments are used to explain the code or to provide the returned values. class(i) # returns "numeric" typeof(i) # returns "double" class(sport) # returns "character" typeof(sport) # returns "character" class(flag) .. returns "logical" tt typeof (flag) # returns "logical" Additional R functions exist that can test the variables and coerce a variable into a specific type. The following R code illustrates how to test if i is an integer using the is . integer ( } function and to coerce i into a new integer variable, j, using the as. integer () function. Similar functions can be applied for double, character, and logical types. is.integer(i) j <- as.integer(i) is.integer(j) # returns FALSE # coerces contents of i into an integer # returns TRUE The application of the length () function reveals that the created variables each have a length of 1. One might have expected the returned length of sport to have been 8 for each of the characters in the string 11 football". However, these three variables are actually one element, vectors. length{i) length(flag) length(sport) # returns 1 # returns 1 # returns 1 (not 8 for "football") 3.1 Introduction to R Vectors Vectors are a basic building block for data in R. As seen previously, simple R variables are actually vectors. A vector can only consist of values in the same class. The tests for vectors can be conducted using the is. vector () function. is.vector(i) is.vector(flag) is.vector(sport) !t returns TRUE # returns TRUE ±t returns TRUE R provides functionality that enables the easy creation and manipulation of vectors. The following R code illustrates how a vector can be created using the combine function, c () or the colon operator, :, to build a vector from the sequence of integers from 1 to 5. Furthermore, the code shows how the values of an existing vector can be easily modified or accessed. The code, related to the z vector, indicates how logical comparisons can be built to extract certain elements of a given vector. u <- c("red", "yellow", "blue") " create a vector "red" "yello•d" "blue" u u[l] v <- 1:5 v sum(v) w <- v * 2 w w[3] ±; t·eturns "red" "yellow'' "blue" returns "red" 1st element in u) # create a vector 1 2 3 4 5 # returns 1 2 3 4 5 It returns 15 It create a vector 2 4 6 8 10 # returns 2 4 6 8 10 returns 6 (the 3rd element of w) z <- v + w z # sums two vectors element by element # returns 6 9 12 15 z > 8
z [z > 8]
# returns FALSE FALSE TRUE TRUE TRUE
# returns 9 12 15
z[z > 8 I z < 5] returns 9 12 15 ("!"denotes "or") Sometimes it is necessary to initialize a vector of a specific length and then populate the content of the vector later. The vector ( } function, by default, creates a logical vector. A vector of a different type can be specified by using the mode parameter. The vector c, an integer vector of length 0, may be useful when the number of elements is not initially known and the new elements will later be added to the end ofthe vector as the values become available. a <- vector(length=3) # create a logical vector of length 3 a # returns FALSE FALSE FALSE b <- vector(mode::"numeric 11 , 3) #create a numeric vector of length 3 typeof(b) # returns "double" b[2] <- 3.1 #assign 3.1 to the 2nd element b # returns 0.0 3.1 0.0 c <- vector(mode= 11 integer", 0) # create an integer vectot· of length o c # returns integer(O) length(c) # returns o Although vectors may appear to be analogous to arrays of one dimension, they are technically dimen- sionless, as seen in the following R code. The concept of arrays and matrices is addressed in the following discussion. REVIEW OF BASIC DATA ANALYTIC METHODS USING R 1·eturns 3 length(b) dim(b) ~ 1·etun1s NULL (an value) Arrays and Matrices The array () function can be used to restructure a vector as an array. For example, the following R code builds a three-dimensional array to hold the quarterly sales for three regions over a two-year period and then assign the sales amount of $158,000 to the second region for the first quarter of the first year. H the dimensions are 3 regions , 4 quarters, and 2 years quarterly_sales < - array(O, dim=c(3,4,2)) quarterly_sales[2,1,11 <- 158000 quarterly_ sales 1 [. 1, [.:! 1 :. , 1 [,·s! [1' 1 0 0 0 [:!,1 !58000 c 0 0 [ 3. 1 0 0 0 0 2 [. 11 (.21 [. 31 [. 4 1 [ 1' 1 0 0 0 0 [2 ' 1 0 0 0 0 [3 ' 1 0 0 0 0 A two-dimensional array is known as a matrix. The following code initializes a matrix to hold the quar- terly sales for the three region s. The parameters nrov1 and nco l define the number of rows and columns, respectively, for the sal es_ma tri x. sales_matrix <- matrix(O, nrow = 3, neal 4) sales_matrix [ 1, 1 [2,1 [ 1' 1 [.11 ;,:!) 1.31 [. .; 1 0 0 0 0 0 0 0 0 n R provides the standard matrix operations such as addition, subtraction, and multiplication, as well as the transpose function t () and the inverse matrix function ma t r ix . inve r s e () included in the matrixcalc package. Th e following R code builds a 3 x 3 matrix, M, and multiplies it by its inverse to obtain the identity matrix. library(matrixcalc) M <- matrix(c(1,3,3,5,0,4,3 , 3,3) ,nrow 3,ncol 3) build a 3x3 matrix 3.1 Introduction toR M %* % matrix . inverse (M} ~ multiply 1·! by inverse (:01 } [. 1] [. 2] [ ' 3] [ 1' J 0 0 [2' J 0 1 0 [3' J 0 0 1 Data Fram es Simi lar to the concept of matrices, data frames provide a structure for storing and accessing several variables of possibly different data types. In fact, as the i s . d ata . fr a me () function indicates, a data frame was created by the r e ad . csv () function at the beginning of the chapter. r.import a CSV :ile of the total annual sales :or each customer s ales < - read . csv ( "c : / data / ye arly_s a l es . c sv" ) i s .da t a . f r ame (sal e s ) ~ t·eturns TRUE As seen earlier, the variables stored in the data frame can be easily accessed using the $ notation. The following R code illustrates that in this example, each variable is a vector with the exception of gende r , which wa s, by a read . csv () default, imported as a factor. Discussed in detail later in this section, a fa ctor denotes a categorical variable, typically with a few finite levels such as "F" and "M " in the case of gender. l e ngth(sal es$num_o f _o r ders) returns 10000 (number of customers) i s . v ector(sales$cust id) returns TRUE - is . v ector(sales$ sales_total) returns TRUE i s .vector(sales$num_of_ orders ) returns TRUE is . v ector (sales$gender) returns FALSE is . factor(s a les$gender ) ~ returns TRUE Because of their fl exibility to handle many data types, data frames are the preferred input format for many ofthe modeling functions available in R. The foll owing use of the s t r () functio n provides the structure of the sal es data frame. This fun ction identifi es the integer and numeric (double) data types, the factor variables and levels, as well as the first few values for each variable. str (sal es) # display structure of the data frame object 'data.ft·ame': 10000 obs . of 4 vanables : $ CUSt id int 100001 100002 100003 100004 100005 100006 . .. $ sales total num 800 . 6 217.5 74.6 498 . 6 723 . 1 $ num of orders : int 3 3 2 3 4 2 2 2 2 2 . .. - $ gender Factor w/ 2 le,·els UfU I "f'-1" : 1 l 2 2 1 1 2 2 1 2 .. . In the simplest sense, data frames are lists of variables of the same length. A subset of the data frame can be re trieved through subsetting operators. R's subsetting operators are powerful in t hat they allow one to express complex operations in a succinct fa shion and easily retrieve a subset of the dataset. '! extract the fourth column of the sales data frame sal es [, 4] H extract the gender column of the sales data frame REVIEW OF BASIC DATA ANALYTIC METHODS USING R sales$gender # retrieve the first two rows of the data frame sales[l:2,] # retrieve the first, third, and fourth columns sales[,c(l,3,4)] l! retrieve both the cust_id and the sales_total columns sales[,c("cust_id", "sales_total")] # retrieve all the records whose gender is female sales[sales$gender=="F",] The following R code shows that the class of the sales variable is a data frame. However, the type of the sales variable is a list. A list is a collection of objects that can be of various types, including other lists. class(sales) "data. frame" typeof(sales) "list" Lists Lists can contain any type of objects, including other lists. Using the vector v and the matrix M created in earlier examples, the following R code creates assortment, a list of different object types. # build an assorted list of a string, a numeric, a list, a vector, # and a matrix housing<- list("own", "rent") assortment <- list("football", 7.5, housing, v, M) assortment [ [1)] [1) "football" [ (2]) [1) 7. 5 [ (3]) [ [ 3)) [ [ 1)) [1) "own" [ [3)) [ [2)] [1) "rent" [ [4)] [1] 1 2 3 4 5 [ [5)] [11 J [21 J [3 1 J [I 1] [ 1 2] [ 1 3 J 1 3 3 5 0 4 3.1 Introduction toR In displaying the contents of assortment, the use of the double brackets, [ [] ] , is of particular importance. As the following R code illustrates, the use of the single set of brackets only accesses an item in the list, not its content. # examine the fifth object, loll in the list class(assortment[S]) .. returns "2.ist" tt length(assortment[S]) .. returns 1 tt class(assortment[[S]]) # returns "matrix" length(assortment[[S]]) # returns 9 {for the 3x3 matrix) As presented earlier in the data frame discussion, the s tr ( ) function offers details about the structure of a list. str(assortment) List of 5 $ : chr "football" $ : num 705 $ :List of 2 0 0 $ : chr "own " 0 0$ : chr "rent" $ int [ 1: 5] 1 2 3 4 5 $ : num [ 1: 3 1 1 : 3] 1 3 3 5 0 4 3 3 3 Factors Factors were briefly introduced during the discussion of the gender variable in the data frame sales. In this case, gender could assume one of two levels: ForM. Factors can be ordered or not ordered. In the case of gender, the levels are not ordered. class(sales$gender) is.ordered(sales$gender) # returns "factor" # returns FALSE Included with the ggplot2 package, the diamonds data frame contains three ordered factors. Examining the cut factor, there are five levels in order of improving cut: Fair, Good, Very Good, Premium, and Ideal. Thus, sales$gender contains nominal data, and diamonds$cut contains ordinal data. head(sales$gender) F F l-1 1'-1 F F Levels: F l\1 library(ggplot2) data(diamonds) # display first six values and the levels # load the data frame into the R workspace REVIEW OF BASIC DATA ANALYTIC METHODS USING R str(diamonds) 'data.frame': 53940 obs. of 10 variables: $ carat $ cut $ color $ clarity: $ depth $ table $ price $ X $ y $ z num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 ... Ord.factor w/ 5 levels "Fair"c"Good"c .. : 5 4 2 4 2 3 ... Ord.factor w/ 7 levels "D"c"E"c"F"c"G"c .. : 2 2 2 6 7 7 Ord.factor w/ 8 levels "I1"c"SI2"c"SI1"< .. : 2 3 5 4 2 num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 num 55 61 65 58 58 57 57 55 61 61 ... int 326 326 327 334 335 336 336 337 337 338 num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ... num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 head(diamonds$cut) # display first six values and the levels Ideal Premium Good Premium Good Very Good Levels: Fair c Good c Very Good < Premium < Ideal Suppose it is decided to categorize sales$sales_ totals into three groups-small, medium, and big-according to the amount of the sales with the following code. These groupings are the basis for the new ordinal factor, spender, with levels {small, medium, big}. # build an empty character vector of the same length as sales sales_group <- vector (mode=''character", length=length(sales$sales_total)) # group the customers according to the sales amount sales_group[sales$sales_total<100] <- "small" sales_group[sales$sales_total>=100 & sales$sales_total<500] <- "medium" sales_group[sales$sales_total>=500] <- "big" # create and add the ordered factor to the sales data frame spender<- factor(sales_group,levels=c("small", "medium", "big"), ordered = TRUE) sales <- cbind(sales,spender) str(sales$spender) Ord.factor w/ 3 levels "small"c"medium"c .. : 3 2 1 2 3 1 1 1 2 1 ... head(sales$spender) big medium small medium big small Levels: small < medium c big The cbind () function is used to combine variables column-wise. The rbind () function is used to combine datasets row-wise. The use of factors is important in several R statistical modeling functions, such as analysis of variance, aov ( ) , presented later in this chapter, and the use of contingency tables, discussed next. 3.11ntrodudion toR Contingency Tables In R, table refers to a class of objects used to store the observed counts across the factors for a given dataset. Such a table is commonly referred to as a contingency table and is the basis for performing a statistical test on the independence of the factors used to build the table. The following R code builds a contingency table based on the sales$gender and sales$ spender factors. # build a contingency table based on the gender and spender factors sales_table <- table{sales$gender,sales$spender) sales_table small medium big F 1726 2746 563 M 1656 2723 586 class(sales_table) typeof(sales_table) dim{sales_table) # performs a chi-squared test summary(sales_table) Number of cases in table: 10000 Number of factors: 2 returns "table" returns "integer" # returns 2 3 Test for independence of all factors: Chisq = 1.516, df = 2, p-value = 0.4686 Based on the observed counts in the table, the summary {) function performs a chi-squared test on the independence of the two factors. Because the reported p-value is greater than 0.05, the assumed independence of the two factors is not rejected. Hypothesis testing and p-values are covered in more detail later in this chapter. Next, applying descriptive statistics in R is examined. 3.1.4 Descriptive Statistics It has already been shown that the summary () function provides several descriptive statistics, such as the mean and median, about a variable such as the sales data frame. The results now include the counts for the three levels of the spender variable based on the earlier examples involving factors. summary(sales) cust ·- id sales - total nurn --- of orders gende1· spender !,lin. :100001 r~lin. 30.02 !\lin. 1.000 F:5035 small :3382 1st Qu. :102501 1st Qu.: 80.29 1st Qu.: 2.000 1<1:4965 medium:5469 !\led ian :105001 r.
~ ~
u
~ ~
c
QJ
:J
0″
QJ
u:
8 -J
~
0 -‘
Age
FIGURE 3-8 Age distribution o f bank account holders
If the age data is in a vector called age, t he graph can be created with the following R script:
h ist(age, b r eaks=l OO , main= “Age Distributi on of Account Holders “,
xlab=”Age”, ylab=”Frequency”, col =”gray” )
The figure shows that the median age of the account holders is around 40. A few accounts with account
holder age less than 10 are unusual but plausible. These could be custodial accounts or college savings
accou nts set up by the parents of young children. These accounts should be retained for future analyses.

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
However, the left side of the graph shows a huge spike of customers who are zero years old or have
negative ages. This is likely to be evidence of missing data. One possible explanation is that the null age
values could have been replaced by 0 or negative values during the data input. Such an occurrence may
be caused by entering age in a text box that only allows numbers and does not accept empty values. Or it
might be caused by transferring data among several systems that have different definitions for null values
(such as NULL, NA, 0, -1, or-2). Therefore, data cleansing needs to be performed over the accounts with
abnormal age values. Analysts should take a closer look at the records to decide if the missing data should
be eliminated or if an appropriate age value can be determined using other available information for each
of the accounts.
In R, the is . na (} function provides tests for missing values. The following example creates a vector
x where the fourth value is not available (NA). The is . na ( } function returns TRUE at each NA value
and FALSE otherwise.
X<- c(l, 2, 3, NA, 4) is.na(x) [1) FALSE FALSE FALSE TRUE FALSE Some arithmetic functions, such as mean ( } , applied to data containing missing values can yield an NA result. To prevent this, set the na. rm parameter to TRUE to remove the missing value during the function's execution. mean(x) [1) NA mean(x, na.rm=TRUE) [1) 2. 5 The na. exclude (} function returns the object with incomplete cases removed. DF <- data.frame(x = c(l, 2, 3), y = c(lO, 20, NA)) DF X y 1 1 10 2 2 20 3 3 NA DFl <- na.exclude(DF) DFl X y 1 1 10 2 2 20 Account holders older than 100 may be due to bad data caused by typos. Another possibility is that these accounts may have been passed down to the heirs of the original account holders without being updated. In this case, one needs to further examine the data and conduct data cleansing if necessary. The dirty data could be simply removed or filtered out with an age threshold for future analyses. If removing records is not an option, the analysts can look for patterns within the data and develop a set of heuristics to attack the problem of dirty data. For example, wrong age values could be replaced with approximation based on the nearest neighbor-the record that is the most similar to the record in question based on analyzing the differences in all the other variables besides age. 3.2 Exploratory Data Analysis Figure 3-9 presents another example of dirty data. The distribution shown here corresponds to the age of mortgages in a bank's home loan portfolio. The mortgage age is calculated by subtracting the orig ina- tion date of the loan from the current date. The vertical axis corresponds to the number of mortgages at each mortgage age. Portfolio Distributio n, Years Since Origination 0 0 "' ~ 0 0 ~ 0 >-u
c
0
(X)
Qj 0
::J 0
cY c:
CT 0
.,
“‘ .,
“‘
0 0 u:
0 “‘ N 0
~
N
0
0
0 0
Oe• OO 1e+05 2e+05 3e+05 4e•05 5e • 05 4 0 4 5 50 55
Income N = 4000 Band\Yidlh = 0 02069
FIGURE 3·11 (a) Histogram and (b) Density plot of household income

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
Figure 3-11 (b) shows a density plot of the logarithm of household income values, which emphasizes
the distribution. The income distribution is concentrated in the center portion of the graph. The code to
generate the two plots in Figure 3-11 is provided next. The rug ( } function creates a one-dimensional
density plot on the bottom of the graph to emphasize the distribution of the observation.
# randomly generate 4000 observations from the log normal distribution
income<- rlnorm(4000, meanlog = 4, sdlog = 0.7) summary (income) Min. 1st Qu. t.Jedian t>!ean 3rd Qu. f.!ax.
4.301 33.720 54.970 70.320 88.800 659.800
income <- lOOO*income summary (income) Min. 1st Qu. f.!edian f.!ean 3rd Qu. 1\!ax. 4301 33720 54970 70320 88800 659800 # plot the histogram hist(income, breaks=SOO, xlab="Income", main="Histogram of Income") # density plot plot(density(loglO(income), adjust=O.S), main="Distribution of Income (loglO scale)") # add rug to the density plot rug(loglO(income)) In the data preparation phase of the Data Analytics Lifecycle, the data range and distribution can be obtained. If the data is skewed, viewing the logarithm of the data (if it's all positive) can help detect struc- tures that might otherwise be overlooked in a graph with a regular, nonlogarithmic scale. When preparing the data, one should look for signs of dirty data, as explained in the previous section. Examining if the data is unimodal or multi modal will give an idea of how many distinct populations with different behavior patterns might be mixed into the overall population. Many modeling techniques assume that the data follows a normal distribution. Therefore, it is important to know if the available dataset can match that assumption before applying any of those modeling techniques. Consider a density plot of diamond prices (in USD). Figure 3-12(a) contains two density plots for pre- mium and ideal cuts of diamonds. The group of premium cuts is shown in red, and the group of ideal cuts is shown in blue. The range of diamond prices is wide-in this case ranging from around $300 to almost $20,000. Extreme values are typical of monetary data such as income, customer value, tax liabilities, and bank account sizes. Figure 3-12(b) shows more detail of the diamond prices than Figure 3-12(a) by taking the logarithm. The two humps in the premium cut represent two distinct groups of diamond prices: One group centers around log 10 price= 2.9 (where the price is about $794), and the other centers around log 10 price= 3.7 (where the price is about $5,012). The ideal cut contains three humps, centering around 2.9, 3.3, and 3.7 respectively. The R script to generate the plots in Figure 3-12 is shown next. The diamonds dataset comes with the ggplot2 package. library("ggplot2") data(diamonds) # load the diamonds dataset from ggplot2 # Only keep the premium and ideal cuts of diamonds .. ' 3t ' ~ o; ; '. " It ' n iceDiamonds < - diamonds [diamonds$cut=="P r emi um" I diamonds$c ut== " Ide a l •, I s ummary(niceDiamonds$cut ) 0 0 Pr m1u 137

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
• Model Evaluation
• Is the model accurate?
• Does the model perform better than an obvious guess?
• Does the model perform better than another cand idate model?
• Model Deployment
• Is the prediction sound?
• Does t he model have the desired effect (such as reduc ing the co st}?
This sec tion discusses some useful statistical tools that may answer these questions.
3.3.1 Hypothesis Testing
When compari ng populations, such as testing or evaluating the difference of the means from two samples
of data (Figure 3-22}, a common technique to assess the difference or the significance of the difference is
hy p oth esis testin g.
FIGURE 3-22 Distribut ions of two samples of data
The basic concept of hypothesis testing is to form an assertion and test it with data. When perform-
in g hypothesis tests, the common assumption is that there is no difference between two samples. This
assumptio n is used as the default position for building the test or conducting a scientific experiment.
Statisticians refer to this as the null hy p o thesis (H
0
). The altern a tive hyp o th esis (H) is that there is a

3.3 Statistical Method s for Evaluation
difference between two samples. For example, if the task is to identify the effect of drug A compared to
drug Bon patients, the null hypothesis and alternative hypothesis would be th is.
• fl0: Drug A and drug B have the same effect on patients.
• fl A: Drug A has a greater effect than drug Bon patients.
If the task is to identify whether advertising Campaign C is effective on reducing customer churn, the
null hypothesis and alternative hypothesis wou ld be as follows.
• fl0 : Campaign C does not reduce customer churn better than the cu rrent campa ign method.
• fl A: Campaign C does reduce customer churn better than the current campa ign.
It is important to state the null hypothesis and alternative hypothesis, because misstating them is likely
to underm ine the subsequent steps of the hypothesis testing process. A hypothesis test leads to either
rejecting the null hypothesis in favor of the alternative or not rejecting the null hypothesis.
Table 3·5 includes some examples of null and alternative hypotheses that should be answered during
the analytic lifecycle.
TABLE 3-5 Example Null Hypotheses and Alternative Hypotheses
Application Null Hypothesis Alternative Hypothesis
Accuracy Forecast
Recommendation
Engine
Regression
Modeling
Model X does not predict better
than the existing model.
Algorithm Y does not produce
better recommendations than
the current algorithm being
used.
This variable does not affect the
outcome because its coefficient
is zero.
Model X predicts better than the existing
model.
Algorithm Y produces better recommen·
dations than t he current algorithm being
used.
This variable affects outcome because its
coefficient is not zero.
Once a model is built over the t raining data, it needs to be eva luated over the testing data to see if the
proposed model predicts better than the existing model curren tly being used. Th e null hypothesis is that
the proposed model does not predict better than the existing model. The alternative hypothesis is that
the proposed model indeed predicts better than the existing model. In accuracy forecast, the null model
could be that the sales of the next month are the same as the prior month. The hypothesis test needs to
evaluate if the proposed model provides a better prediction. Take a recommendation engine as an example.
The null hypothesis could be that the new algorithm does not produce better recommendations than the
current algorithm being deployed. The alternative hypothesis is that the new algorithm produces better
recommendations than the old algorithm.
When eva luating a model, sometimes it needs to be determined if a given input variable improves the
model. In regression analysis (Chapter 6), for example, this is the same as asking if the regression coefficient
for a variable is zero. The null hypothesis is that the coefficient is zero, which means the variable does not
have an impact on the outcome. The alternative hypothesis is that the coefficient is nonzero, which means
the variable does have an impact on the outcome.

REVIEW OF BASIC DATA ANALYTIC METHODS USING R
A common hypothesis test is to compare the means of two populations. Two such hypothesis test s are
discussed in Section 3.3.2.
3.3.2 Difference of Means
Hypothesis testing is a common approach to draw inferences on whether or not the two populations,
denoted pop1 and pop2, are different from each other. This section provides two hypothesis tests to com-
pare the means of the respective populations based on sam ples randomly drawn from each population.
Specifically, the two hypothesis tests in this section consider the following null and alternative hypotheses.
• Ho: II , = ll 2
• HA: II , “”‘ ll2
The 1’ , and 11
2
denote the population means of pop1 and pop2, respectively.
The basic testing approach is to compare the observed sample means, X, and X
2
, corresponding to each
population. If the values of X
1
and X
2
are approximately equal to each other, the distributions of X, and
X2 overlap substantially (Figure 3-23), and the null hypothesis is supported. A large observed difference
between the sample means indicates that the null hypothesis should be rejected. Formally, the difference
in means can be tested using Student’s t-test or the Welch’s t-test.
Irx, ‘” :X2 .
this area is
large
FIGURE 3-23 Overlap of the two distributions is larg e if X1 ::::: X2
Student’s t-test
Stud ent ‘s t- test ass umes that distributions of t he t wo populations have equal but unknow n
varian ces. Suppose n
1
and n
2
samples are random ly and independently selected from two populations,
pop1 and pop2, respectively. If each population is normally distributed with the same mean (Jt
1
= Jt
2
) and
wi th the sa me variance, then T (the t-statistic ), given in Equation 3-1, follows a t -distribution w ith
n, + n
2
– 2 degrees of f reed om (df).
where (3-1)

3.3 Statistical Methods for Evaluation
The shape of the t-distribution is similar to the normal distribution. In fact, as the degrees of freedom
approaches 30 or more, the t-distribution is nearly identical to the normal distribution. Because the numera-
tor ofT is the difference of the sample means, if the observed value ofT is far enough from zero such that
the probability of observing such a value of Tis unlikely, one would reject the null hypothesis that the
population means are equal. Thus, for a small probability, say a= 0.05, T* is determined such that
P(ITI2: T*) = 0.05. After the samples are collected and the observed value ofT is calculated according to
Equation 3-1, the null hypothesis (p,1 = p2) is rejected ifiTI2: r·.
In hypothesis testing, in general, the small probability, n, is known as the significance level of the test.
The significance level of the test is the probability of rejecting the null hypothesis, when the null hypothesis
is actually TRUE.In other words, for n = 0.05, if the means from the two populations are truly equal, then
in repeated random sampling, the observed magnitude ofT would only exceed r· 5% of the time.
In the following R code example, 10 observations are randomly selected from two normally distributed
populations and assigned to the variables x andy. The two populations have a mean of 100 and 105,
respectively, and a standard deviation equal to 5. Student’s t-test is then conducted to determine if the
obtained random samples support the rejection of the null hypothesis.
# generate random observations from the two populations
x <- rnorm(lO, mean=lOO, sd=S) # normal distribution centered at 100 y <- rnorm(20, mean=lOS, sd=S) ll no~:mal distribution centered at 105 t.test(x, y, var.equal=TRUE) Two Sample t-test data: x and y # run the Student's t-test t = -1.7828, df = 28, p-value = 0.08547 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -6.1611557 0.4271393 sample estimates: mean of x mean of y 102.2136 105.0806 From the R output, the observed value of Tis t = -1.7828. The negative sign is due to the fact that the sample mean of xis less than the sample mean of y. Using the qt () function in R, a Tvalue of 2.0484 corresponds to a 0.05 significance level. # obtain t value for a two-sidec test at a 0.05 significance level qt(p=O.OS/2, df=28, lower.tail= FALSE) 2.048407 Because the magnitude of the observed T statistic is less than the T value corresponding to the 0.05 significance level Q -1.78281< 2.0484), the null hypothesis is not rejected. Because the alternative hypothesis is that the means are not equal (p 1 :;z:: 11 2 ), the possibilities of both p, > 11
2
and p 1 < 112 need to be considered. This form of Student's t-test is known as a two-sided hypothesis test, and it is necessary for the sum of the probabilities under both tails of the t-distribution to equal the significance level. It is customary to evenly REVIEW OF BASIC DATA ANALYTIC METHODS USING R divide the significance level between both tails. So, p = 0.05/2 = 0.025 was used in the qt () function to obtain the appropriate t-value. To simplify the comparison of the t-test results to the significance level, the R output includes a quantity known as the p -value. ln the preceding example, the p-value is 0.08547, which is the sum of P(T ~ - 1.7828) and P(T ~ 1.7828). Figure 3-24 illustrates the t-statistic for the area under the tail of a t-distribution. The -t and tare the observed values of the t-statistic. ln the R output, t = 1.7828. The left shaded area corresponds to the P(T ~ - 1.7828), and the right shaded area corresponds to the P(T ~ 1.7828). -t 0 FIGURE 3-24 Area under the tails (shaded) of a student's t-distribution In the R output, for a significance level of 0.05, the nu ll hypothesis would not be rejected because the likelihood of a Tvalue of magnitude 1.7828 or greater would occur at higher probability than 0.05. However, based on the p -value, if the significance level was chosen to be 0.10, instead of 0.05, the null hypothesis would be rejected. In general, the p-value offers the probability of observing such a sample result given the null hypothesis is TRUE. A key assumption in using Student's t-test is that the population variances are equal. In the previous example, the t . test ( ) function call includes var . equal=TRUE to specify that equa lity of the vari- ances should be assumed. If that assumption is not appropriate, then Welch's t-test should be used. Welch 's t-test When the equal population variance assumption is not justified in performing Student's t-test for the dif- ference of means, Welch's t-test [14] can be used based on T expressed in Equation 3-2. (3-2) where X,. 5,2, and n, correspond to the i-th sample mean, sample variance, and sample size. Notice that Welch's t-test uses the sample va riance (5ll for each population instead of the pooled sample variance. In Welch's test, under the remaining assumptions of random samples from two normal populations with the same mea n, the distribution of Tis approximated by the t-distribution. The following R code performs the We lch's t-test on the same set of data analyzed in the ea rlier Student's t-test example. 3.3 Statistical Methods for Evaluation t.test(x, y, var.equal=FALSE) # run the Welch's t-test l'lelch Two Sample t-test data: x andy t = -1.6596, df = 15.118, p-value = 0.1176 alternative hypothesis: true difference in neans is not equal to o 95 percent confidence interval: -6.546629 0.812663 sample estimates: mean of x mean of y 102.2136 105.0806 In this particular example of using Welch's t-test, the p-value is 0.1176, which is greater than the p-value of 0.08547 observed in the Student's t-test example. In this case, the null hypothesis would not be rejected at a 0.10 or 0.05 significance level. It should be noted that the degrees of freedom calculation is not as straightforward as in the Student's t-test. In fact, the degrees of freedom calculation often results in a non-integer value, as in this example. The degrees of freedom for Welch's t-test is defined in Equation 3-3. df=l~r l~:r --+-- n,-1 n2 -1 (3-3) In both the Student's and Welch's t-test examples, the R output provides 95% confidence intervals on the difference of the means. In both examples, the confidence intervals straddle zero. Regardless of the result of the hypothesis test, the confidence interval provides an interval estimate of the difference of the population means, not just a point estimate. A confidence interval is an interval estimate of a population parameter or characteristic based on sample data. A confidence interval is used to indicate the uncertainty of a point estimate.lfx is the estimate of some unknown population mean f..L, the confidence interval provides an idea of how close xis to the unknown p. For example, a 95% confidence interval for a population mean straddles the TRUE, but unknown mean 95% of the time. Consider Figure 3-25 as an example. Assume the confidence level is 95%. If the task is to estimate the mean of an unknown value Jt in a normal distribution with known standard deviation u and the estimate based on n observations is x, then the interval x ± ~ straddles the unknown value of Jl with about a 95% chance. If one takes 100 different samples and computes the 95% confi- dence interval for the mean, 95 of the 100 confidence intervals will be expected to straddle the population mean Jt. REVIEW OF BASIC DATA ANALYTIC METHODS USING R FIGURE 3-25 A 95% confidence interval straddlin g the unknown population mean 1J Confidence intervals appear again in Section 3.3.6 on AN OVA. Return ing to t he discussion of hypoth- es is t est ing, a key assumpti on in b oth t he Stud ent 's and Welch 's t-tes t is that the relevant population attri bute is norma lly distributed. For non-norm ally dist ributed data, it is sometimes p ossible to transform the co llected data to approx imate a normal distribution . For example, taki ng the logarithm of a d ataset can often transfo rm skewed d ata to a dataset that is at least symmetric arou nd its mean. Howeve r, if such transform ations are ineffective, there are tes t s like t he Wi lcoxon ra nk-su m test that can be ap plied to see if t wo population distributions are different. 3.3.3 Wilcoxon Rank-Sum Test At-test represents a parametric test in t hat it makes assumptions about the population distributions f rom w hich th e sa mples are drawn. If t he populations cann ot be assu med or transformed to follow a normal distribut ion, a n onparametric test can be used . The Wilcoxo n rank-sum test [15] is a nonpa ramet ric hypothesis test that checks w hether two populations are identically d istributed. Assuming the two popula- t ions are identica lly distributed, o ne would expect that the ordering of any sampled observations would be evenly intermixed among t hemselves. For example, in orderi ng the observations, one would not expect to see a large number of observations from one population grouped together, especially at the beginning or the end of ordering. Let the t wo p opulations again be popl and pop2, w ith independently random samples of size n 1 and n 2 respecti vely. The tot al number of observations is then N = n 1 + n 2 • The first step of the Wilcoxon t est is to rank t he set of observat ions from t he t wo groups as if they came from one la rge group. The smallest observation receives a rank of 1, t he second smallest observation receives a rank of 2, and so on with the largest observatio n being assig ned the rank of N. Ties amo ng the observations receive a ran k equal to t he average of the ranks they span. The test uses ranks instead of numerical o utcom es t o avoid specific assumpt io ns about the shape of the distributi on. After ranking all the ob serva t ions, t he assig ned ranks are summed for at least one population's sample. If t he distribution of popl is shift ed to t he right of the other distribution, t he rank-sum corr espondi ng to popl's sa mple shou ld be larger than the rank-sum of pop2. The Wilcoxon rank- sum test determines the 3.3 Statistical Methods for Evaluat io n significance of the observed rank-su ms. The following R code performs the test on the same dataset used for the previous t-test. wilcox.test(x, y, conf.int TRUE) :·:~.c :-c n r rJ.: ! 1 t The wilcox. test ( l function ranks the observations, determines the respective rank-sums cor- responding to each population's sample, and then determines the probability of such rank-sums of such magnitude being observed assuming that the population distributions are identical. In this example, the probability is given by the p-value of 0.04903. Thu s, the null hypothesis would be rejected at a 0.05 sig- nificance level. The reader is cautioned against interpreting that one hypothesis test is clearly better than another test based solely on the examples given in this section. Because the Wilcoxon test does not assume anything about the population distribution, it is generally considered more robust than the t-test. In other words, there are fewer assumptions to violate. However, when it is reasonable to assume that the data is normally distributed, Student's or Welch's t-test is an appropriate hypothesis test to consider. 3.3.4 Type I and Type II Erro rs A hypothesis test may result in two types of errors, depending on whether the test accepts or rejects the null hypothesis. These two errors are known as type I and type II errors. • A type I error is the rejection of the null hypothesis when the null hypothesis is TRUE. The probabil- ity of the type I error is denoted by the Greek letter n . • A type II error is the acceptance of a null hypothesis when the null hypothesis is FALSE. The prob- ability of the type II error is denoted by the Greek letter .1. Table 3-61ists the four possible states of a hypothesis test, including the two types of errors. TABLE 3-6 Type I and Type II Error H 0 is true H 0 is false H 0 is accepted Correct outcome Type II Error H 0 is rejected Type/error Correct outcome REVIEW OF BASI C DATA ANALYTIC METHO DS USING R The significance level, as mentioned in the Student's t-test discussion, is equivalent to the type I error. For a significance level such as o = 0.05, if the null hypothesis (Jt 1 = J1 1) is TRUE, there is a So/o chance that the observed Tvalue based on the sample data will be large enough to reject the null hypothesis. By select- ing an appropriate sig nificance level, the probability of commi tting a type I error can be defined before any data is collected or analyzed. The probability of committing a Type II error is somewhat more difficult to determine. If two population means are truly not equal, the probability of committing a type II error will depend on how far apart the means truly are. To reduce the probability of a type II error to a reasonable level, it is often necessary to increase the sample size. This topic is addressed in the next section. 3.3.5 Power and Sample Size The power of a test is the probability of correctly rejecting the null hypothesis. It is denoted by 1- /.3, where f] is the probability of a type II err or. Because the power of a test improves as the sample size increases, power is used to determine the necessary sample size. In the difference of means, the power of a hypothesis test depends on the true difference of the po pulation means. In ot her words, for a fixed significance level, a larger sample size is required to detect a smaller difference in the means. In general, the magn itude of the difference is known as the effect size. As the sample size becomes larger, it is easier to detect a given effec t size, 6, as illustrated in Figure 3-26. Moderate Sample Size Larger Sample Size 1------1 1------1 a' a' F IGURE 3-26 A larger sample size better identifies a fixed effect size With a large enough sample size, almost any effect size can appear statistica lly sign ificant. However, a very small effect size may be useless in a practical sense. It is import an t to consider an appropriate effect size for the problem at hand. 3.3.6ANOVA The hypothesis tests presented in the previous sections are good for analyzing means between two popu- lations. But what if there are more than two populations? Consider an examp le of testing the impact of 3.3 Statistical Methods for Evaluation nutrition and exercise on 60 candidates between age 18 and 50. The candidates are randomly split into six groups, each assigned with a different weight loss strategy, and the goal is to determine which strategy is the most effective. o Group 1 only eats junk food. o Group 2 only eats healthy food. o Group 3 eats junk food and does cardia exercise every other day. o Group 4 eats healthy food and does cardia exercise every other day. o Group 5 eats junk food and does both cardia and strength training every other day. o Group 6 eats healthy food and does both cardia and strength training every other day. Multiple t-tests could be applied to each pair of weight loss strategies. In this example, the weight loss of Group 1 is compared with the weight loss of Group 2, 3, 4, 5, or 6. Similarly, the weight loss of Group 2 is compared with that of the next 4 groups. Therefore, a total of 15 t-tests would be performed. However, multiplet-tests may not perform well on several populations for two reasons. First, because the number oft-tests increases as the number of groups increases, analysis using the multiplet-tests becomes cognitively more difficult. Second, by doing a greater number of analyses, the probability of committing at least one type I error somewhere in the analysis greatly increases. Analysis of Variance (ANOVA) is designed to address these issues. AN OVA is a generalization of the hypothesis testing of the difference of two population means. AN OVA tests if any of the population means differ from the other population means. The null hypothesis of A NOVA is that all the population means are equal. The alternative hypothesis is that at least one pair of the population means is not equal. In other words, 0 Ho:Jll = J12 = ··· = Jln o H A: Jl; ::= J1i for at least one pair of i,j As seen in Section 3.3.2, "Difference of Means," each population is assumed to be normally distributed with the same variance. The first thing to calculate for the AN OVA is the test statistic. Essentially, the goal is to test whether the clusters formed by each population are more tightly grouped than the spread across all the populations. Let the total number of populations be k. The total number of samples N is randomly split into the k groups. The number of samples in the i-th group is denoted as n 1 , and the mean of the group is X1 where iE[l,k]. The mean of all the samples is denoted as X 0 • The between-groups mean sum of squares, s;, is an estimate of the between-groups variance. It measures how the population means vary with respect to the grand mean, or the mean spread across all the populations. Formally, this is presented as shown in Equation 3-4. k 52 =-1-~n.·(x.-x )2 8 k-1L...i I I 0 1=1 (3-4) The within-group mean sum of squares, s~. is an estimate of the within-group variance. It quantifies the spread of values within groups. Formally, this is presented as shown in Equation 3-5. REVIEW OF BASIC DATA ANALYTIC METHODS USING R (3-5) If s; is much larger than 5~, then some of the population means are different from each other. The F-test statistic is defined as the ratio of the between-groups mean sum of squares and the within- group mean sum of squares. Formally, this is presented as shown in Equation 3-6. (3-6) The F-test statistic in A NOVA can be thought of as a measure of how different the means are relative to the variability within each group. The larger the observed F-test statistic, the greater the likelihood that the differences between the means are due to something other than chance alone. The F-test statistic is used to test the hypothesis that the observed effects are not due to chance-that is, if the means are significantly different from one another. Consider an example that every customer who visits a retail website gets one of two promotional offers or gets no promotion at all. The goal is to see if making the promotional offers makes a difference. ANOVA could be used, and the null hypothesis is that neither promotion makes a difference. The code that follows randomly generates a total of 500 observations of purchase sizes on three different offer options. offers<- sample(c("offerl", "offer2", "nopromo"), size=SOO, replace=T) # Simulated 500 observations of purchase sizes on the 3 offer options purchasesize <- ifelse(offers=="offerl", rnorm(SOO, mean=SO, sd=30), ifelse(offers=="offer2", rnorm(SOO, mean=SS, sd=30), rnorm(SOO, mean=40, sd=30))) # create a data frame of offer option and purchase size offertest <- data.frame(offer=as.factor(offers), purchase_amt=purchasesize) The summary ofthe offertest data frame shows that 170 offerl, 161 offer2, and 169 nopromo (no promotion) offers have been made. It also shows the range of purchase size (purchase_ amt) for each of the three offer options. # display a summary of offertest where o:fer="offer1" summary(offertest[offertest$offer=="offerl",]) offer nopromo: offe:n :170 offer2 : purchase_amt t·li;;.. 4.521 1 s:: Qu . : 5 8 . 1 5 8 i·iedian : 76. 944 I·1ean .Sl. 936 3 rd Qu. : 1 D 4 . 9 59 t•la:·:. :130.507 # display a summary of offertest where o:fer="offer2" summary(offertest[offertest$offer=="offer2",]) offer nopromo: 0 offer! 0 offer2 :161 purchase_amt ~lin. 14.04 1st Qu . : 6 9 . 4 6 t·ledian : 90.20 r•lean 89.09 3 rd Qu. : 10 7. 4 8 !•lax. : 154. 3 3 # display a summary of offertest where offer="nopromo" summary(offertest[offertest$offer== 11 nopromo 11 ,]) offer nopromo:169 offerl 0 offer2 : 0 purchase_amt Min. :-27.00 1st Qu.: 20.22 tF)
offers 2 225222 112611 130.6 <2e-16 Residuals 4 97 428470 862 Signif. codes: 0 1 *** 1 0.001 1 **' 0.01 1 * 1 0.05 '. 1 0.1 1 1 1 The output also includes the 5~ (112,611), 5~ (862), the F-test statistic (130.6), and the p-value (< 2e-16). The F-test statistic is much greater than 1 with a p-value much less than 1. Thus, the null hypothesis that the means are equal should be rejected. However, the result does not show whether offerl is different from offer2, which requires addi- tional tests. The TukeyHSD (} function implements Tukey's Honest Significant Difference (HSD) on all pair-wise tests for difference of means. TukeyHSD(model) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aoviformula purchase amt - offers, data $offers diff lwr upr offertest) p adj offerl-nopromo 40.961437 33.4638483 48.45903 0.0000000 REVIEW OF BASIC DATA ANALYTIC METHODS USING R offer2 nop romo 48 . 120286 40 . 51894~6 55 . 72163 O. OOOOOCO offer2--fferl 7 . 1"8849 -0 . 4315769 14 . 7 4928 o . r692895 The re sult includes p -values of pair-wise comparisons of the three offer options. The p-values for of ferl- nopromo and of fer- nop romo are equal to 0, smaller than the significance level 0.05. Thi s suggests t hat both of ferl and offer2 are sign ifica ntly different from n opromo. A p-value of 0.0692895 for off er2 aga inst of fer 1 is greater than the significance level 0.05. This suggests that of fer2 is not significantly different from offerl. Because only the influence of one fa ctor (offers) was executed, the presented A NOVA is known as one- way ANOVA. If the goal is to analyze two factors, such as offers and day of week, that would be a two-way A NOVA [16]. 1f the goal is to model more than one outcome variable, then multivariate AN OVA (or MAN OVA) cou ld be used. Summary R is a popu lar package and prog ramming language for data exploration, analytics, and visualization. As an introduction toR, this chapter covers t he R GUI, data 1/0, attribute and data types, and descriptive statistics. This chapter also discusses how to useR to perform exploratory data analysis, including the discovery of dirty data, visua lization of one or more variables, and customization of visualization for different audiences. Finally, the chapter introduces some basic statistical methods. The firs t statistical method presented in the chapter is the hypothesis testing. The Student's t-test and Welch's t-test are included as two example hypoth- esis tests designed for testing the difference of means. Other statistical methods and tools presented in this chapter include confidence interva ls, Wilcoxon rank-sum test, type I and II errors, effect size, and ANOVA. Exercises 1. How ma ny levels does fdata contain in the following R code? data = c(1 , 2,2,3,1,2 , 3,3 ,1 , 2,3,3 , 1) fdata = factor(data) 2. Two vectors, vl and v2, are created with the following R code: vl <- 1:5 v2 <- 6 : 2 What are the results of cbi nd (vl , v2) and rbind (vl , v2)? 3. What R comma nd(s) would you use to remove null values from a dataset? 4. What R command can be used to install an additiona l R package? 5. What R fun ction is used to encode a vector as a ca tegory? 6. What is a rug plot used for in a density plot? 7. An online retailer wa nts to study the purchase behaviors of its customers. Figure 3-27 shows the den- sity plot of the purchase sizes (in dol lars) . What wou ld be your recommendation to enhance the plot to detect more structures that otherwise might be missed? Bibliography Be-04 6e-04 £ "' ~ 4e-04 2e-04 09+(}0 0 2000 4000 6000 8000 10000 purchase size (dollars) fiGURE 3-27 Density plot of purchase size 8. How many sections does a box-and-whisker divide the data into? What are these sections? 9. What attributes are correlated according to Figure 3-18? How would you describe their relationships? 10. What function can be used to tit a nonlinear line to the data? 11. If a graph of data is skewed and all the data is positive, what mathematical technique may be used to help detect structures that might otherwise be overlooked? 12. What is a type I error? What is a type II error? Is one always more serious than the other? Why? 13. Suppose everyone who visits a retail website gets one promotional offer or no promotion at all. We want to see if making a promotional offer ma kes a difference. What statistical method wou ld you recommend for this analysis? 14. You are ana lyzing two norma lly distributed populations, and your null hypothesis is that the mean f1 1 of the first population is equal to the mean 112 of the second. Assume the significance level is set at 0.05. If the observed p ·value is 4.33e-05, what will be your decision regarding the null hypothesis? Bibliography [1] The R Project for Statistical Computing, "R Licenses." [Online). Available: http : I l www. r- proj ec t. orgiLicensesl. [Accessed 10 December 2013]. [2] The R Project for Statistical Computing, "The Comprehensive R Arch ive Network." [Onli ne]. Available: http: I lcran . r-project. orgl. [Accessed 10 December 2013]. REVIEW OF BASIC DATA ANALYTIC METHODS USING R [3] J. Fox and M. Bouchet-Valat, "The R Commander: A Basic-Statistics GUI for R," CRAN. [Online]. Available: http : I / soc s e rv . mcmaste r. ca / j fox / Misc / Rcmdr / . [Acce ssed 11 December 2013]. [4] G. William s, M. V. Culp, E. Cox, A. Nolan, D. White, D. Medri, and A. Waljee, "Rattle: Graphical User Interface for Data Mining in R," CRAN. [Online]. Available: ht t p : I I c ran . r - p roj ect . org/ we b / pac kages / rattl e/ index . html. [Accessed 12 December 2013]. [5] RStudio, "RStudio IDE" [Online]. Available: http : I / www. rstudio . com/ide / . [Accessed 11 December 2013]. [6] R Special Interest Group on Databases (R-SIG-DB), "OBI: R Database Interface." CR AN [On line]. Available: http : I I cran . r-proj ec t. o rg/ we b / packages / DBI / index . h tml . [Accessed 13 December 2013]. (7] B. Ripley, "RODBC: ODBC Database Access," CRAN. [Online]. Available: http : 11 cran . r-pr o j- e c t . o rg/ web / packages / RODBC / index. html. [Accessed 13 December 2013]. [8] S. S. Stevens, "On the Theory of Scales of Measurement," Science, vol. 103, no. 2684, p. 677-680, 1946. [9] D. C. Hoaglin, F. Mosteller, and J. W. Tukey, Understanding Robust and Exploratory Data Analysis, New York: Wi ley, 1983. [10) F. J. Anscom be, "G raphs in Statistical Analysis," The American Statistician, vol. 27, no. 1, pp. 17- 21, 1973. [11) H. Wickham, "ggplot2," 2013. [Online]. Availa ble: h ttp: I I ggplo t2 . org I . [Accessed 8 January 2014]. [12) W. S. Cleveland, Visualizing Data, Lafayette, IN: Hobart Press, 1993. [13] R. A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, vol. 7, no. 2, pp. 179- 188, 1936. [14) B. L. Welch, "The Generalization of "Student's" Problem When Several Different Popu lation Variances Are Involved," Biometrika, vol. 34, no. 1-2, pp. 28- 35, 1947. [15) F. Wilcoxon, "Individual Comparisons by Ranking Methods," Biometrics Bulletin, vol. 1, no. 6, pp. 80- 83, 1945. [16) J. J. Faraway, "Practical Regression and A nova Using R," July 2002. [On line]. Available: ht tp : 11 c ran. r-pro ject . o r g / doc/ c o ntrib/ Fa r awa y - PRA. pdf. [Accessed 22 January 2014]. ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERING Building upon the introduction toR presented in Chapter 3, "Review of Basic Data Analytic Methods Using R," Chapter 4, "Advanced Analytical Theory and Methods: Cl ustering" through Chapter 9, "Advanced Analytical Theory and Methods: Tex t Analysis" describe several commonly used analytical methods that may be considered for the Model Planning and Execution phases (Phases 3 and 4) of the Data Analytics Lifecycle. This chapter considers clustering techniques and algorithms. 4.1 Overview of Clustering In general, clustering is the use of unsupervised tech niques for grouping sim ilar objects. In mach ine learning, unsupervi sed refers to the problem of finding hidden structure withi n unlabeled data. Clustering techniques are unsupervised in the sense that the data scientist does not determine, in advance, the labels to apply to the clusters. The structure of the data describes the objects of interest and determines how best to group the object s. For example, based on customers' personal income, it is straightforward to divide the customers into three groups depending on arbitrarily selected values. The customers could be divided into three groups as follows: • Earn less than $10,000 • Earn between 510,000 and $99,999 • Earn $100,000 or more In this case, the income leve ls were chosen somewhat subjectively based on easy-to-commun icate points of de lineation. However, such groupings do not indicate a natural affinity of the customers within each group. In other word s, there is no inherent reason to believe that the customer making $90,000 will behave any differently than the customer making 5110,000. As additional dimensions are introduced by adding more variables about the customers, the ta sk of finding meaningful groupings becomes more complex. For instance, suppose variables such as age, years of education, household size, and annual purchase expenditures were considered along with the personal income variable. What are t he natural occurring groupings of customers? This is the type of question that clustering analysis can help answer. Clustering is a method often used for exploratory ana lysis of the data. In clustering, there are no pre- dictions made. Rather, clustering methods find the sim ilarities between objec ts according to the object attributes and group the simi lar objects into clusters. Clustering techniques are utilized in marke ti ng, economics, and various branches of science. A popular clustering method is k-means. 4.2 K-means Given a collection of objects each with n measurable attributes, k-means [1] is an analytical technique that, for a chose n value of k, identifies k clusters of obj ects based on the objects' proximity to the center of the k groups. The center is determined as the arithmetic average (mean) of each cluster's n-dimensiona l vector of attributes. This section describes the algorithm to determ ine the k means as well as how best to apply this technique to several use cases. Figure 4-1 illustrates three clusters of objects with two attributes. Each object in the dataset is represented by a small dot color-coded to the closest large dot, the mean of the cluster. 4 .2K-means • • • •••• • •• • •• • • • • • • • • • • ~ 0 • • • e• o 0 • e o• • • • • 0 0 • • • 0 ••• ••• • 0 0 0 • • ,8 • e0o o • 0 0 0 0 fiGURE 4-1 Possible k-means clusters for k=3 4.2.1 Use Cases Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its chara cteristics. Classification is covered in more detai l in Chapter 7, "Adva nced Analytical Theory and Methods: Classification." Clustering is primarily an exploratory technique to discover hidden structures of the data, possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing, medical, and customer segmentation. Image Processing Video is one example of the growi ng volumes of unstructured data being collected. Within each frame of a video, k-means analysis ca n be used to identify objects in the video. For each frame, the task is to deter- mine which pixels are most similar to each other. The attributes of each pixel ca n include bri ghtness, color, and location, th e x and y coordinates in the frame. With security video images, for example, successive frames are examined to identify any cha nges to the clusters. These newly identified clusters may indicate unauthorized access to a facility. Medical Patient attributes such as age, height, weight, systolic and diastolic blood pressures, cholesterol level, and other attributes can identify naturally occurring clusters. These clusters could be used to ta rget individuals for specific preventive measures or clinica l trial participation. Clustering, in general, is useful in biology for the classification of plants and animals as well as in the field of human genetics. ADVANCED ANALYTICAL THEORY AND METHODS: CLU STERI NG Customer Segmentation Marketing and sa les groups use k-means to better identify customers who have similar behaviors and spending patterns. For example, a wireless provider may look at the following customer attributes: monthly bill, number of text messages, data volume consumed, minutes used during various daily periods, and years as a customer. The wi reless company could then look at the naturally occurring clusters and consider tactics to increase sales or reduce the customer ch urn rate, the proportion of customers who end their relationship with a particular company. 4.2.2 Overview of the Method To illustrate the met hod to find k clusters from a collection of M objects wit h n attributes, t he two- dimensional case (n = 2) is examined. It is much easier to visualize the k-means method in two dimensions. later in the chapter, the two-dimension scenario is generalized to handle any number of attributes. Because each object in this example has two attributes, it is usefu l to consider each object correspond - ing to the point (x,. y) , where x andy denote the two attributes and i = 1, 2 ... M. For a given cluster of m points (m ~M), the point that corresponds to the cluster's mean is called a centroid. In mathematics, a centroid refers to a point that corresponds to the center of mass for an object. The k-means algorithm to find k clusters can be described in the following four steps. 1. Choose the value of k and the k initial guesses for the centroids. In this example, k = 3, and the initial centroids are indicated by the points shaded in red, green, and blue in Figure 4-2. 0 0 0 0 0 0 0 o 0 8 0 00 0 0 0 0 0 0 0 0 0 0 3. There fore,
the k-means analysis wi ll be conducted fork = 3. The process of identifying the appropriate va lue of k is
referred to as finding the “elbow” of the WSS curve.

km kmeans(kmdata,3, nstart~2S)
km
K-means clustering with 3 clusters of sizes 158, 218, 244
Cluster means:
English Math Science
1 97.21519 93.37342 94.86076
2 73.22018 64.62844 65.84862
3 85.84426 79.68033 81.50820
Clustering vector:
[1] 1 1 1 1 1 1
1 1 1 1 1
[41] 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1
1 1
1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1
[81] 1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1
1 1 1 1 1 1 1 1 1 1 1 1 1
[121] 1 1 1 1 1
3 3 3 3 3
[161] 3
1
1
1
3
1 1
3 3
3
1 1 1 1 1 1 1 1 1 1 1
3 3 3 3 1 1 3
[201] 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3
[241] 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3
[281] 3 3 3 3 3 3 3
3 3 3 3 3 3 3
[321] 3
3
[361] 3
3
3 3 3
3 3 3
3
3
2 2 2 2 3 3 2 2 2 2
3
3
3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3
3 3 3 3 2 2 2 2 2 2 2
1 1 1 1 1
1 3 3
3 3 3
3 3 3
3 3 3 3 3
3 3 3 3 3
2 3 2 3 3 3
[401] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2
[441] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2
2 2 2 2 2 2 2 2 3 2
[481] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2
[521] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2
[561] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2
[601] 3 2 2 3 1 1 3 3 3 2 2 3 2
Within cluster sum of squares by cluster:
[1] 6692.589 34806.339 22984.131
(between_SS I total_SS 76.5 %)
Available components:
[1] “cluster” “centers” “totss”
[6] “betweenss” “size” “iter”
“withinss”
“ifault”
“tot.withinss”
4.2K-means

ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERING
The displayed contents of the variable km include the following:
o The location of the cluster means
o A clustering vector that defines the membership of each student to a corresponding cluster 1, 2, or 3
o The WSS of each cluster
o A list of all the available k-means components
The reader can find details on these components and using k-means in R by employing the help facility.
The reader may have wondered whether the k-means results stored in km are equivalent to the WSS
results obtained earlier in generating the plot in Figure 4-5. The following check verifies that the results
are indeed equivalent.
c( wss[3] , sum(km$withinss)
[1] 64483.06 64483.06
In determining the value of k, the data scientist should visualize the data and assigned clusters. In the
following code, the ggplot2 package is used to visualize the identified student clusters and centroids.
#prepare the student data and clustering results for plotting
df; as.data.frame(kmdata_orig[,2:4])
df$cluster ; factor(km$cluster)
centers;as.data.frame(km$centers)
gl; ggplot(data=df, aes(x;English, y=Math, color=cluster )) +
geom_point() + theme(legend.position=”right”) +
geom_point(data=centers,
aes(x;English,y=Math, color=as.factor(c(l,2,3))),
size=lO, alpha=.3, show_guide=FALSE)
g2 =ggplot(data=df, aes(x=English, y=Science, color=cluster )) +
geom _point () +
geom_point(data=centers,
aes(x=English,y=Science, color=as.factor(c(l,2,3))),
size=lO, alpha=.3, show_guide=FALSE)
g3 = ggplot(data=df, aes(x=Math, y=Science, color=cluster )) +
geom_point () +
geom_point(data=centers,
aes(x;Math,y=Science, color=as.factor(c(l,2,3))),
size=lO, alpha;.), show_guide=FALSE)
tmp ggplot_gtable(ggplot_build(gl))

100 –
..c:: 90 –
ro so –
::2 – o –
eo –
Q) 100 –
(.) 90 –
~ 80 –
u -o –
en eo –
Q) 100 –
(.) 90 –
~ so –
(.) 70 –
en eo –
grid.arrange(arrangeGrob(gl + theme(legend.position=”none”),
g2 + theme(legend.position=”none”),
g3 + theme(legend.position= “none ” ),
main =”High School Student Cluster Analysis” ,
ncol=l) )
4.2K-means
The resul ting plots are provided in Figure 4-6. The large circles represent the loca tion of the cluster
means provided earlier in the display of the kmcontents. The small dots represent the students correspond-
ing to the appropriate cluster by assigned color: red, blue, or green. In general, the plots indicate the three
clusters of students: the top academic students (red), the academical ly cha llenged students (green), and
the other students (b lue) who fall somewhere between those two groups. The plots also high lig ht which
students may excel in one or two subject areas but struggle in othe r areas.
High School Student Cluster Analysis
• • • •
•
• •
• • • • • • • : • • I • • I •
~~;.:,······ -=· : ~ : : I I I I i I I I I ! I I
I I
eo
• •
•
. I. . . . . I . . :1 •!::!• a . : ‘ : .
I
eo
70
• •
so
Engli sh
•
, . •.. d, ll ll:
• • • a I · • I a • : : • • • • • e I
I I
70 so
English
Math
9D
90
• • • •
100
;pt11!
• • •
100
FIGURE 4-6 Plots of the identified student clust ers
Assig ning labels to the identified clusters is useful to communicate the results of an analysis. In a mar-
keting context, it is common to label a group of customers as frequent shoppers or big spenders. Such
designations are especia lly useful when communicating the clustering results to business users or execu-
tives. It is better to describe the marketing plan for big spenders rather than Cluster #1.

ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERING
4.2.4 Diagnostics
The heuristic using WSS can provide at least several possible k values to consider. When the num ber of
attributes is relatively small, a common approach to further refine the choice of k is to plot the data to
determine how distinct the identified clusters are from each other. In general, the following questions
should be considered.
• Are the clusters well separated from each other?
• Do any of the clusters have only a few poi nts?
• Do any of the centroids appear to be too close to each other?
In the first case, ideally t he plot would look like the one shown in Figure 4-7, when n = 2. The clusters
are well defined, with considerable space between the fo ur identified clusters. However, in other cases,
such as Figure 4-8, the clusters may be close to each other, and the distinction may not be so obvious.
0
0~ ~C>.>
–
«:
N <%>0 00 <§ 0 @0(/>
I I I I
2 4 6 8
X
FIGURE • -7 Example of distinct clusters
In such cases, it is important to apply some judgment on whether anything different will result by using
more clusters. For example, Figure 4-9 uses six clusters to describe the sa me dataset as used in Figu re 4-8.
If using more clusters does not better distinguish the groups, it is almost certainly better to go with fewer
clusters.

4.2 K-means
0
0
n
0 0 0 fJ ,j
(X) 0
0 0 0
Oo o o 0
00 0 C• 0
10 0 0 C> 0
0 0
oo 0
>- 0
0 0
0 C• 0
0
0
~
00
0
0 0
8
0
0
0 0 0 0
N ~ 0 0
0
0 0
T I I
2 4 6 8
X
F IGURE 4-8 Example of less obvious clusters
0
0 0 8 0 f) 0
(X) – 0
0 0
Oo oo 0
0 0
0
10 – 0 0 0
0 0
0
>- 0 0
•:1 0 0
0
-:r – 0 0 0
8
0
0
0 0 0 0
N ~ 0 0
0
0 0
I
2 4 6 8
X
FIGURE 4-9 Six clusters applied to the points from Figure 4-8

ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERING
4.2.5 Reasons to Choose and Cautions
K-means is a simple and straightforward method for defining clusters. Once clusters and their associated
centroids are identified, it is easy to assign new objects (for example, new customers) to a cluster based on
the object’s distance from the closest centroid. Because the method is unsupervised, using k-means helps
to eliminate subjectivity from the analysis.
Although k-means is considered an unsupervised method, there are still several decisions that the
practitioner must make:
o What object attributes should be included in the analysis?
o What unit of measure (for example, miles or kilometers) should be used for each attribute?
o Do the attributes need to be rescaled so that one attribute does not have a disproportionate effect on
the results?
o What other considerations might apply?
Object Attributes
Regarding which object attributes (for example, age and income) to use in the analysis, it is important
to understand what attributes will be known at the time a new object will be assigned to a cluster. For
example, information on existing customers’ satisfaction or purchase frequency may be available, but such
information may not be available for potential customers.
The Data Scientist may have a choice of a dozen or more attributes to use in the clustering analysis.
Whenever possible and based on the data, it is best to reduce the number of attributes to the extent pos-
sible. Too many attributes can minimize the impact of the most important variables. Also, the use of several
similar attributes can place too much importance on one type of attribute. For example, if five attributes
related to personal wealth are included in a clustering analysis, the wealth attributes dominate the analysis
and possibly mask the importance of other attributes, such as age.
When dealing with the problem of too many attributes, one useful approach is to identify any highly
correlated attributes and use only one or two of the correlated attributes in the clustering analysis. As
illustrated in Figure 4-10, a scatterplot matrix, as introduced in Chapter 3, is a useful tool to visualize the
pair-wise relationships between the attributes.
The strongest relationship is observed to be between Attribute3 and Attribute7.lfthe value
of one of these two attributes is known, it appears that the value of the other attribute is known with
near certainty. Other linear relationships are also identified in the plot. For example, consider the plot of
Attribute2 against Attribute3.lfthe value of Attribute2 is known, there is still a wide range of
possible values for At tribu t e3. Thus, greater consideration must be given prior to dropping one of these
attributes from the clustering analysis.

4.2 K-mean s
Another option to reduce the number of attributes is to combine several attributes into one measure.
For example, instead of using two attribute variables, one for Debt and one for Assets, a Debt to Asset ra tio
could be used. This option also addresses the problem when the magnitude of an attribute is not of real
interest, but the relative magnitude is a more important measure.
FIGURE 4-10 Scatterplot matrix for seven attributes

ADVANCED ANALYTICAL T H EORY AND METHODS: CLUSTERI NG
Uni t s of Measure
From a computational perspective, the k-means algorithm is somewhat indifferent to the units of measure
for a given attribute (for example, meters or centimeters for a patient’s height). However, the algorithm
will identify different clusters depending on the choice of the units of measure. For example, suppose that
k-means is used to cluster pat ients based on age in years and height in centimeters. For k=2, Figure 4-11
illustrates the two clusters that would be determined for a given dataset.
0
0
0 –
N
E’ 0
~ lO –
J:
01
0 00 0% 00 0 0 00 g> 0

~ o o 00 ~ o
0 ooooo>CX> o o o o
o Ooo 0 o 0
Qi 0
r. 0 –
0
lO –
0
I I I I
0 20 40 60 80
age (years)
FIGURE 4-11 Clusters with height expressed in centimeters
But if the height was rescaled from centimeters to meters by dividing by 100, the resulting clusters
would be slightly different, as illustrated in Figure 4-12.
0
0
I
0 20 40 60 80
age (years)
FIGURE 4-12 Clusters with height expressed in meters

4 .2K·means
When the height is expressed in meters, the magnitude of the ages dominates the distance calculation
between two points. The height attribute provides only as much as the square between the difference of the
maximum height and the minimum height or (2.0 – 0)2 = 4 to the rad icand, the number under the square
root symbol in the distance formu la given in Eq uation 4·3. Age ca n contribute as much as(S0 – 0)2 = 6,400
to the radicand when measuring the distance.
Rescaling
Attributes that are ex pressed in dollars are common in clustering analyses and can differ in magnit ude
from the other attributes. For example, if personal income is expressed in dollars and age is expressed in
years, the income attribute, often exceeding $10,000, can easily dominate the distance calculation with
ages typically less than 100 years.
Although some adjustments could be made by expressing the income in thousands of dollars (for
example, 10 for $10,000), a more straightforward method is to divide each attribute by th e attribute’s
sta nda rd deviation. The resulting attributes wi ll each have a standa rd deviation equal to 1 and wi ll be
without units. Returning to the age and height example, the standard deviations are 23.1 years and 36.4
em, respectively. Dividing each attribute val ue by the appropri ate standard deviation and performing the
k-means analysis yields the result shown in Figure 4-13.
0
0
I I I I
00 OS 10 1 5 20 25 30 35
age (rescaled)
FIGURE4 13 Clusters with rescaled attributes
With the resca led attributes for age and height, the borders of the resulting clusters now fall somewhere
between the two earlier clustering analyses. Such an occurrence is not surpri sing based on the magnitudes
of the attributes of the previous clusteri ng attempts. Some practitioners also subtract the means of the
attributes to center the attributes around zero. However, this step is unnecessa ry because the distance
formu la is only sensitive to the scale of the attribute, not its location.

ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERING
In many statistical analyses, it is com mon to transform typically skewed data, such as income, with long
tails by taking the logarithm of the data. Such transformation can also be appied ink-means, but the Data
Scientist needs to be aware of what effect th is transformation will have. For example, if /og
10
of income
expressed in dollars is used, the practitioner is essentially stating that, from a clustering perspective, $1,000 is
as cl ose to S10,000as $10,000 is to $100,000 (because log10 1,000 = 3,1og10 10,000 = 4, and log10 100,000 = 5).
In many cases, the skewness of the data may be the reason to perform the clustering analysis in the first place.
Additional Consideration s
The k-means algorithm is sensitive to the starting positions of the initial centroid. Thus, it is important to
rerun the k-means analysis several times for a particular value of k to ensure the cluster results provide the
overa ll minimum WSS. As seen earlier, this task is accompl ished in R by using the nstart option in the
kmeans () function call.
This chapter presented the use of the Euclidean distance function to assign the points to the closest cen-
troids. Other possible function choices include the cosine similarity and the Manhattan distance fun ctions.
The cosine similarity function is often chosen to compare two documents based on the frequency of each
word that appea rs in each of the documents [2). For two points, p and q, at (p
1
, pl’ … p
0
) and (q
1
, q
2
, .• • q
0
),
respectively, the Manhattan distance, d
1
, between p and q is expressed as shown in Equation 4-6.
n
dl(p.q) = L h – q~ (4-6)
I I
The Manhattan dista nce function is analogous to the distance traveled by a car in a city, where the
streets are laid out in a rectangular grid (such as city blocks). In Euclidean distance, the measurement is
made in a straight line. Using Equation 4-6, the distance from (1, 1) to (4, 5) would be J1 – 41 + J1 – 51 = 7.
From an optimi zation perspectiv e, if there is a need to use the Manhattan distan ce for a clustering analysis,
the median is a better choice for the centroid than use of the mean [2).
K-means cl ustering is applicable to obj ects that can be described by attributes that are numerical with
a meaningful distance measure. From Chapter 3, interval and ratio attribute types can certai nly be used.
However, k-means does not handle categorical variables well. For example, suppose a clustering analysis
is to be conducted on new car sales. Among other attributes, such as the sale price, the color of the car is
considered important. Although one could assign numerical values to the color, such as red = 1, yellow
= 2, and green = 3, it is not useful to consider that ye llow is as close to red as yellow is to green from a
clustering perspective. In such cases, it may be necessary to use an alte rn ative clustering methodology.
Such methods are described in the next section.
4.3 Additional Algorithms
The k-means clustering method is easily applied to numeric data where the concept of distance can natu rally
be applied. However, it may be necessary or desirable to use an alternative clustering algorithm. As discussed
at the end of the previous section, k-means does not handle categorical data. In such cases, k-modes [3) is a
common ly used method for clustering categorical data based on the number of differences in the respective
components of the attribute s. For example, if each obj ect has four attributes, the distance from (a, b, e, d)
to (d, d, d, d) is 3. In R, the function kmode () is implemented in the klaR package.

Exercises
Because k-means and k-modes divide the entire dataset into di stinct groups, both approaches are
considered partitioning methods. A third partitioning method is known as Partitioning around Medoids
(PAM) [4). In general, a medoid is a representative object in a set of objects. in cl usteri ng, the medoids are
the objects in each cluster that minimize the sum of the distances from the medoid to the other objects
in the cluster. The advantage of using PAM is that the “center” of each cluster is an actual object in the
dataset. PAM is implemented in R by the pam () function included in the cluster R package. The fpc
R package includes a function pamk () ,which uses the pam () function to find the optimal value fork.
Other clustering methods include hierarchical agglomerative clustering and density clustering methods.
In hierarchical agglomerative clustering, each object is initially placed in its own cluster. The clusters are
then combined with the most similar cluster. This process is repeated until one cluster, which includes all
the objects, exists. The R stats package includes the h clust () function for performing hierarchical
agglomerative clustering. in density-based clustering methods, the clusters are identified by the concentra-
tion of points. The fpc R package includes a function, dbscan () ,to perform density-based clustering
analysis. Density-based clustering can be useful to identify irregularly shaped clusters.
Summary
Clustering analysis groups similar objects based on the objects’ attributes. Clustering is applied in are as
such as marketing, economics, biology, and medicine. This chapter presented a detailed explanation of the
k-means algorithm and its implementation in R. To use k-means properly, it is important to do the following:
• Properly scale the attribute values to prevent certain attributes from dominating the other attributes.
• Ensure that the concept of dista nce between the assigned values within an attribute is meaningful.
• Choose the number of clusters, k, such that the sum of the Within Sum of Squares (WSS) of the
distances is reasonably minimized. A plot such as the exam ple in Figu re 4-5 can be helpfu l in this
respect.
If k-means does not appear to be an appropriate clustering technique for a given dataset, then alterna-
tive techniques such as k-modes or PAM should be considered.
Once the clusters are identified, it is often useful to label these clusters in some descriptive way. Especially
when dealing with upper management, these labels are useful to easily communicate the findings of the
clustering analysis. In clusteri ng, the labels are not preassigned to each object. The labels are subjectively
assigned after the clusters have been identified. Chapter 7 considers severa l methods to perform the cl as-
sification of objects with predetermined labels. Clustering can be used with other analytical techniques,
such as regre ssion. Linear regression and logistic regression are covered in Chapter 6, “Advanced Analytica l
Theory and Methods: Regression.”
Exercises
1. Using the age and height clustering example in section 4.2.5, algebraically illustrate the impact on the
measured distance when the height is expressed in meters rather than centimeters. Explain why different
clusters will result depending on the choice of units for the patient’s height.
2. Compare and contrast five clustering algorithms, assigned by the instructor or selected by the student.

ADVANCED ANALYTICAL THEORY AND METHODS: CLUSTERI NG
3. Using the ruspini dataset provided with the cl us ter package in R, perform a k-means analysis.
Document the findings and j ustify the choice of k. Hint: use data ( ruspini ) to load the dataset into
the R workspace.
Bibliography
[1] J. MacQueen, “Some Methods for Classification and Analysis of Multivari ate Observa tions,” in
Proceedings of th e Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA,
1967.
[2] P.-N. Tan, V. Kumar, and M. Steinbach, Introduction to Data Mining, Upper Saddle River, NJ: Person,
2013.
[3] Z. Huang, “A Fast Clustering Algorithm to Cluster Very Large Categorica l Data Sets in Data Mining,”
1997. [Onlin e] . Available: http : I I ci teseerx . is t . psu . edulviewdoc l
download ? d o i=1 0 . 1. 1 . 13 4. 83&rep=rep1 &type =pdf. [Accessed 13 March 2014].
[4] L. Kaufman and P. J. Rous seeuw, “Partitioning Around Medoids (Program PAM),” in Finding Groups
in Data: An Introduction to Cluster Analysis, Hoboken, NJ, John Wiley & Sons, Inc, 2008, p. 68-125,
Chapter 2.

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
This chapter discusses an unsupervised learning method called association rules. This is a descriptive, not
predictive, method often used to discover interesting relationships hidden in a large dataset. The disclosed
relationships can be represented as rules or frequent itemsets. Association rules are commonly used for
mining transactions in databases.
Here are some possible questions that association rules can answer:
• Which products tend to be purchased together?
• Of those customers who are similar to this person, what products do they tend to buy?
• Of those customers who have purchased this product, what other similar products do they tend to
view or purchase?
5.1 Overview
Figure 5-l shows the general logic behind association rules. Given a large collection of transactions (depicted
as three stacks of receipts in the figure), in which each transaction consists of one or more items, associa-
tion rules go through the items being purchased to see what items are frequently bought together and to
discover a list of rules that describe the purchasing behavior. The goal with association rules is to discover
interesting relationsh ips among the items. (The relationship occu rs too frequently to be random and is
meaningful from a business perspective, which may or may not be obvious.) The relationships that are inter-
esting depend both on the business context and the natu re of the algorithm being used for the discovery.
:.• … …
:.•
WIUIO ‘iitl rtot
Jl tJint’t’ –.••
t:UC~~o. •• t:US
lHU110 .,_,.,,,,
‘0 &Ol”
FIGURE 5-1 The genera/logic behind association rules
Cereal
Bread
Milk
Milk
Rules
.. Milk (90%)
.. Milk (40%)
.. Cereal (23%)
.. Apples (10%)
Wine .. Diapers (2%)
Each of the uncovered rules is in the form X ~ Y, meaning that when item X is observed, item Y is
also observed. In this case, the left-hand side (LHS) of the rule is X, and the right-hand side (RHS) of the
rule is Y.
Usi ng association rules, patterns ca n be discovered from the data that allow the association ru le algo-
rithms to disclose rules of related product purchases. The uncovered rules are listed on the right side of

5.1 Overview
Figure 5-1. The first three rules suggest that when cereal is purchased, 90% of the time milk is purchased
also. When bread is purchased, 40% of the time milk is purchased also. When milk is purchased, 23% of
the time cereal is also purchased.
In the example of a retail store, association rules are used over transactions that consist of one or
more items. In fact, because of their popularity in mining customer transactions, association rules are
sometimes referred to as market basket analysis. Each transaction can be viewed as the shopping
basket of a customer that contains one or more items. This is also known as an item set. The term itemset
refers to a collection of items or individual entities that contain some kind of relationship. This could be
a set of retail items purchased together in one transaction, a set of hyperlinks clicked on by one user in
a single session, or a set of tasks done in one day. An item set containing k items is called a k-itemset.
This chapter uses curly braces like { i tern 1, i tern 2, . . . i tern k} to denote a k-itemset.
Computation of the association rules is typically based on item sets.
The research of association rules started as early as the 1960s. Early research by Hajek et al. [1] intro-
duced many of the key concepts and approaches of association rule learning, but it focused on the
mathematical representation rather than the algorithm. The framework of association rule learning was
brought into the database community by Agrawal et al. [2] in the early 1990s for discovering regularities
between products in a large database of customer transactions recorded by point-of-sale systems in
supermarkets. In later years, it expanded to web contexts, such as mining path traversal patterns [3] and
usage patterns [4] to facilitate organization of web pages.
This chapter chooses Apriori as the main focus of the discussion of association rules. Apriori [5] is
one of the earliest and the most fundamental algorithms for generating association rules. It pioneered
the use of support for pruning the itemsets and controlling the exponential growth of candidate item-
sets. Shorter candidate item sets, which are known to be frequent item sets, are combined and pruned
to generate longer frequent itemsets. This approach eliminates the need for all possible item sets to be
enumerated within the algorithm, since the number of all possible itemsets can become exponentially
large.
One major component of Apriori is support. Given an item set L, the support [2] of Lis the
percentage of transactions that contain L. For example, if 80% of all transactions contain item set
{bread}, then the support of {bread} is 0.8. Similarly, if 60% of all transactions contain itemset
{bread, butter}, then the support of {bread, butter} is 0.6.
A frequent itemset has items that appear together often enough. The term “often enough” is for-
mally defined with a minimum support criterion. If the minimum support is set at 0.5, any itemset can
be considered a frequent item set if at least 50% of the transactions contain this itemset. In other words,
the support of a frequent itemset should be greater than or equal to the minimum support. For the
previous example, both {bread} and {bread, butter} are considered frequent item sets at the
minimum support 0.5. If the minimum support is 0.7, only {bread} is considered a frequent itemset.
If an item set is considered frequent, then any subset of the frequent item set must also be frequent.
This is referred to as the Apriori property (or downward closure property). For example, if 60% of the
transactions contain {bread, jam}, then at least 60% of all the transactions will contain {bread} or
{jam}. In other words, when the support of {bread, jam} is 0.6, the support of {bread} or { jam}
is at least 0.6. Figure 5-2 illustrates how the A priori property works. If item set { B, c, D} is frequent, then
all the subsets of this itemset, shaded, must also be frequent itemsets. The Apriori property provides the
basis for the Apriori algorithm.

ADVANCED AN ALYTICAL THEORY AND METHODS: ASSOCIATION RULES
FIGURE 5-2 Item set {A, B,C,D} and its subsets
5.2 Apriori Algorithm
The Apriori algorithm takes a bottom -up itera tive approach to uncovering the frequent itemsets by fi rs t
determ ining al l the possible items (o r 1-itemsets, for exam ple {bread}, {eggs}, {milk}, .. . ) and
then identifyi ng which among them are frequent.
Assuming the minim um support threshold (or the minimum support criterion) is set at 0.5, the algo-
rithm identifies and reta ins t hose itemsets that appear in at least 50% of all tran sactions and discards (or
“prunes away”) the itemsets that have a support less than 0.5 or appear in fewer than 50% of the trans-
actions. The word prune is used like it would be in gardening, where unwa nted branches of a bush are
clipped away.
In the next iteration of the Apriori algorithm, the identified freq uent 1-itemsets are pai red into
2-itemsets (for example, {bread, eggs} , {bread , milk }, {eggs, milk), … ) and again eval u-
ated to identify the frequent 2-itemsets among them.
At each iteration, the algorithm checks whether the support criterion can be met; if it can, the algorithm
grows the itemset, repeating the process until it run s out of support or until the itemsets reach a predefi ned
length. The A priori algorithm [5] is given next. Let va ri able Ck be the set of candidate k-item sets and variable
Lk be the set of k-itemsets that satisfy the minimum support. Given a t ransaction database 0, a minimum
support threshold /5 , and an optional parameter N indicating t he maximum length an itemset could reach,
Apri ori iteratively computes freq uent itemsets Lk . 1 based on Lk.
1 Aprio ri (0 , 6, N )
2 k-1
3 L,- {1-itemsets that satisfy minimum support 6}
4 while L, “”0
s if ~N V (3N 1\ k < N) 5.3 Evaluation of Candidate Rules 6 C~t- 1 .... candi dat e i t emsets generate d from Lk 7 f or each t r a n sac tion t in database 0 do B increment the counts of Ck _ 1 con taine d i n 9 Lk~ l ~ can d idate s i n Ck+l that satisfy minimum s upport 8 10 k - k + l 11 return U L k k The first step of the A priori algorithm is t o identify the f requent item sets by starting w ith each item in the transactions that meets the predefi ned minimum support threshold 8. These itemsets are 1-itemsets denoted as~. as eac h 1- itemset contai ns only one item. Next, the algorithm grows the it em set s by joining ~onto itself to form new, grown 2-itemsets denot ed as~ and determines the support of each 2-itemset i n ~- Those itemsets that do not meet the minimum support threshold 8 are pruned away. The growing and pruni ng process is repeated u ntil no itemset s meet th e minimum suppo rt threshold. Opti onally, a threshold N can be set up to specify the maxi mum number of items the item set can reach or the maximum number of iterations of the algorithm. Once completed, o utput of the Apriori algori t hm is the coll ection of all the frequent k-itemsets. Next, a co llection of candid ate rules is formed based o n the frequent itemsets uncovered in the itera- tive process described earli er. For examp le, a freq uent itemset {mi lk, egg s} may sugg est candidate rules {mi lk}-+{ eggs } and {eg gs} -. {mil k} . 5.3 Evaluation of Candidate Rules Freq uent itemsets from the previous section can form candidate ru les such as X implies Y (X -7 Y). This section discusses how measures such as confidence, li ft, and leverage can help evaluate the appropriate- ness of t hese candidate rules. Confidence [2] is defined as the measure of certai nty or t ru st worthiness associated with each discov- ered rule. Mathematically, confidence is the percent of transactions that contain both X and Y out of all the transactions t hat contain X (see Equation 5-1). C ,.d ( X Y) Support(X 1\ Y) on11 ence -+ = --'-'------'- Support( X) (S-1) For example, if {b r e a d, e g gs , milk} has a support ofO.lS and {bread , eggs} also has a support of0.15, the co nfidence of rule (bread, egg s} ->{ milk} is 1, wh ich means 100% of the time a customer
buys bread and eggs, milk is bought as well. The rule is therefore correc t for 100% of the transactions
containing b rea d and egg s.
A relationship may be t hought of as interesting when the algorithm identifi es the relationship w ith a
measure of confidence greater than or equal t o a pred efi ned threshold. This predefined threshold is called
the minimum confidence. A hig her confidence indicates that the rul e (X -7 Y) is more interesting or more
trustworthy, based o n the sam ple dat aset.
So fa r, th is chapter has t alked about t wo common measures that the A priori algorithm uses: support
and confidence. All t he rul es can be ranked based on these t wo measures t o filter o ut the uninteresting
rule s and re tain the interesting ones.

ADVANCED ANALmCAL THEORY AND METHODS: ASSOCIATION RULES
Even though confidence can identify the interesting rules from all the candidate rules, it comes with
a problem. Given rules in the form of X ~ Y, confidence considers only the antecedent (X) and the co-
occurrence of X and Y; it does not take the consequent of the rule (Y) into concern. Therefore, confidence
cannot tell if a rule contains true implication of the relationship or if the rule is purely coincidental. X and
Y can be statistically independent yet still receive a high confidence score. Other measures such as lift [6]
and leverage [7] are designed to address this issue.
Lift measures how many times more often X and Y occur together than expected if they are statisti-
cally independent of each other. lift is a measure [6] of how X andY are really related rather than coinci-
dentally happening together (see Equation 5-2).
Lift(X -t Y) = Support(X 1\ Y)
Support( X)* Support(Y)
(5-2)
Lift is 1 if X and Y are statistically independent of each other. In contrast, a lift of X ~ Y greater than 1
indicates that there is some usefulness to the rule. A larger value of lift suggests a greater strength of the
association between X and Y.
Assuming 1,000 transactions, with {milk, eggs} appearing in 300 ofthem, {milk} appearing in
500, and {eggs} appearing in 400, then Lift(milk -t eggs)= 0.3/(0.5 *0.4) = 1.5. If {bread} appears
in 400 transactions and {milk, bread} appears in 400, then Lift(milk -t bread)=0.4/{0.5*0.4) =2.
Therefore it can be concluded that milk and bread have a stronger association than milk and eggs.
Leverage [7] is a similar notion, but instead of using a ratio, leverage uses the difference (see
Equation 5-3). Leverage measures the difference in the probability of X and Y appearing together in the
dataset compared to what would be expected if X and Y were statistically independent of each other.
Leverage (X -t Y) =Support (X 1\ Y)- Support( X)* Support(Y) (5-3)
In theory, leverage is 0 when X and Yare statistically independent of each other. If X and Y have some
kind of relationship, the leverage would be greater than zero. A larger leverage value indicates a stronger
relationship between X andY. For the previous example, Leverage(milk -t eggs)= 0.3-(0.5*0.4) = 0.1
and Leverage(milk -t bread)=0.4-(0.5*0.4) = 0.2.1t again confirms that milk and bread have a stronger
association than milk and eggs.
Confidence is able to identify trustworthy rules, but it cannot tell whether a rule is coincidental. A
high-confidence rule can sometimes be misleading because confidence does not consider support of
the itemset in the rule consequent. Measures such as lift and leverage not only ensure interesting rules
are identified but also filter out the coincidental rules.
This chapter has discussed four measures of significance and interestingness for association rules:
support, confidence, lift, and leverage. These measures ensure the discovery of interesting and strong
rules from sample datasets. Besides these four rules, there are other alternative measures, such as corre-
lation [8], collective strength [9], conviction [6], and coverage [10]. Refer to the Bibliography to learn how
these measures work.

5.5 An Example: Transactions in a Grocery Store
5.4 Applications of Association Rules
The term market basket analysis refers to a specific implementation of association rules min ing t hat
many companies use for a variety of purposes, including these:
• Broad-scale approaches to better merchandising- what products should be included in or excluded
from the inventory each month
• Cro ss-merchand ising between products and high-margin or high -ticket items
• Physical or logical placement of product within related categori es of products
• Promotional programs-m ultiple product purchase incentives managed through
a loyalty card program
Besides market basket analysis, association rules are commonly used for recommender systems [11 ]
and clickstream analysis [12].
Many online service providers such as Amazon and Netflix use recommender systems. Recommender
systems can use association rules to discover related products or identify customers who have similar
interests. For exa mple, association rules may suggest that those customers who have bought product A
have also bought product B, or those customers who have bought products A, B, and Care more similar
to this customer. These fin dings provide opportunities for re tailers to cross-sell their products.
Clickstrea m analysis refers to the analytics on data related to web browsing and user clicks, which
is stored on the client or the server side. Web usage log files generated on web servers contain huge
amounts of information, and association rules can potentially give useful knowledge to web usage data
analys ts. For example, association rul es may suggest that website visitors who land on page X click on
links A, B, and C much more often than links 0, E, and F. This observation provides val uable insight on
how to better personalize and recommend the content to site visitors.
The next section shows an example of grocery store transactions and demonstrates how to useR to
perform association rule mining.
5.5 An Example: Transactions in
a Grocery Store
An example illustrates the application of the A priori algorithm to a relatively si mple case that generalizes
to those used in practice. Using Rand the a r ules and arul esVi z packages, this example shows how
to use the A priori algorithm to generate frequent itemsets and rules and to evaluate and visual ize the rules.
The following commands install these two packages and import them into the current R workspace:
ins t a ll.packages ( ‘ aru les’ )
ins tall.pa ckages ( ‘arulesViz’ )
library ( ‘ a rul es ‘ )
l i b r ary( ‘ arulesViz ‘ )

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
5.5.1 The Groceries Dataset
The example uses the Groceries dataset from the R arules package. The Groceries dataset is
collected from 30 days of real-world point-of-sale transactions of a grocery store. The dataset contains
9,835 transactions, and the items are aggregated into 169 categories.
data(Groceries)
Groceries
transactions in sparse format with
9835 transactions lrowsl and
169 items (columns)
The summary shows that the most frequent items in the dataset include items such as whole milk,
other vegetables, rolls/buns, soda, and yogurt. These items are purchased more often than the others.
summary(Groceries)
transactions as itemMatrix in sparse format with
9835 rows (elements/itemsets/transactionsl and
169 columns !items) and a density of 0.02609146
most frequent items:
whole milk other vegetables
2513 1903
y·ogurt
1372
(Other)
34055
rolls/buns
1809
element (itemset/transaction) length distribution:
sizes
1 2 4 5 6 7 9 10
2159 1643 1299 1005 855 645 545 438 350 246
15 16 17 18 19 20 21 22 23 24
55 46 29 14 14 9 ll 4 6
32
1
Min. 1st Qu. !•led ian r•lean 3rd Qu. !\lax.
1. 000 2.000 3.000 4.409 6.000 32.000
includes extended item information – examples:
labels level2 levell
1 frankfurter sausage meet and sausage
2 sausage sausage meet and sausage
liver loaf sausage meet and sausage
soda
1715
11 12 13 14
132 117 78 77
26 27 28 29
l 3
The class of the dataset is transactions, as defined by the arules package. The
transactions class contains three slots:
o transac tioninfo: A data frame with vectors of the same length as the number of transactions
o i teminfo: A data frame to store item labels
o data: A binary incidence matrix that indicates which item labels appear in every transaction

class(Groceries)
(1] “transactions”
att!: 1:, “package” ,I
[1] “arules”
5.5 An Example: Transactions in a Grocery Store
For the Groceries dataset, the transactioninfo is not being used. Enter
Groceries®i teminfo to display all169 grocery labels as well as their categories. The following
command displays only the first 20 grocery labels. Each grocery label is mapped to two levels of catego-
ries-level2 and level1-where level1 is a superset of level2. For example, grocery label
sausage belongs to the sausage category in level2, and it is part of the meat and sausage
category in levell. (Note that “meet” in level1 is a typo in the dataset.}
Groceries®iteminfo[1:20,]
leveL::
frankfurter sa’Jsage
2 sa’Jsagt: sausa9e
l i ·.rer loal sausage
4 han sausage
5 meat. sausage
6 finished products sausage
7 organic sausaqe sausage
8 chicken p::::ul tl·:-·
9 ~u::..-key·· poultry
10 pod: n::cd:
11 beef beef
12 hamburger meat beef
13 fish fish
1 •l citrus fruit fc,lit
15 tropical :ruit fr:.:i t
16 pip fn:it fr–.:i::
17 grapes fruit
18 berries fruit
19 nuts/prunes fruit
20 root vegetables vegetables
meet a:–.a sa·l:~~age
meet and sa~~Qge
meet and sa~~age
meet and sat:o:;aqc
meet ar,d sai:::,\yc
meet and Sc’lL’dLJe
mee~ a:1d sau~~· .. :=aqe
mee:: a:1d sau,,c;ge
meet and sausage
meet and sausage
ftui t and vegel’lbl·:::s
,.reget~iL’l~s
~/eg~t ~d:~ l~s
veget ai; l es
vegetal’ 1 es
… Jegetal_}les
f:uit anG
t !_”‘Ll_:_ t a.r:d
– !.”tl.i t_ a:1d
fn1i t a:1d
fruit and
fn.tit and vegetables
The following code displays the lOth to 20th transactions of the Groceries dataset. The
[ 1 o : 2 o] can be changed to [ 1 : 9 8 3 5 J to display all the transactions.
apply(Groceries®data[,l0:20], 2,
function(r) paste(Groceries®iteminfo[r, ”labels 11 ), collapse= 11 , 11 )
Each row in the output shows a transaction that includes one or more products, and each transaction
corresponds to everything in a customer’s shopping cart. For example, in the first transaction, a cus-
tomer has purchased whole milk and cereals.
[1] “whole milk, cereals”
[2] “tropical fruit, other vege·:ables, •:ihite ht•·:d, bottled \•later,
chocolate”
[3] “citrus fruit, tropical fc·u:t, ·.·:hole :nil;.:, i·.1:ter, curd, ‘iogurt,
flour, bottled ·.·:ater, d::.shes”
[4] “beef”

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
[5] “frankfurter, rolls/buns, soda”
[6] “chicken, tropical fruit”
(7] “butter, sugar, fruit/vegetable juice, newspapers”
[B] “fruit/vegetable juice”
[9] “packaged fruit/vegetables”
[10] “chocolate”
[11] “specialty bar”
The next section shows how to generate frequent itemsets from the Groceries dataset.
5.5.2 Frequent ltemset Generation
The apriori (} function from the arule package implements the Apriori algorithm to create frequent
itemsets. Note that, by default, the apriori (} function executes all the iterations at once. However, to
illustrate how the Apriori algorithm works, the code examples in this section manually set the parameters
of the apriori (} function to simulate each iteration of the algorithm.
Assume that the minimum support threshold is set to 0.02 based on management discretion.
Because the dataset contains 9,853 transactions, an item set should appear at least 198 times to be
considered a frequent item set. The first iteration of the Apriori algorithm computes the support of each
product in the dataset and retains those products that satisfy the minimum support. The following code
identifies 59 frequent 1-itemsets that satisfy the minimum support. The parameters of apr ior i ( )
specify the minimum and maximum lengths of the item sets, the minimum support threshold, and the
target indicating the type of association mined.
itemsets <- apriori(Groceries, parameter;list(minlen=l, maxlen=l, support=0.02, target= 11 frequent itemsets 11 )) parame:er specification: confidence minval smax arem aval originalSupport support minlen 0.8 0.1 1 none FALSE TRUE 0.02 1 max len target ext 1 frequent itemsets FALSE algorithmic control: filter tree heap memopt load sort verbose 0.1 TRUE TRUE FALSE TRUE 2 TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt set item appearances ... [0 item(s)) done [O.OOs]. set transactions ... [169 item(s), 9835 transaction(s)] done [O.OOs]. sorting and recoding items ... [59 item(s) J done [O.OOs]. creating transaction tree ... done [O.OOs]. checking subsets of size 1 done [O.OOs]. writing ... [59 set(s)) done [O.OOs). creating S4 object ... done [0. oos]. 5.5 An Example: Transactions in a Grocery Store The summary of the itemsets shows that the support of Htemsets ranges from 0.02105 to 0.25552. Because the maximum support of the 1-itemsets in the dataset is only 0.25552, to enable the discovery of interesting rules, the minimum support threshold should not be set too close to that number. surnrnary(iternsets) set of 59 itemsets most frequent items: frankfurter sausage 1 1 (Other) 54 ham 1 neat 1 chicken 1 element (itemset/transaction) length distribution:sizes 1 59 Min. 1st Qu. Median 1 Mean 3rd Qu. 1 1 summary of quality measures: support Min. :0.02105 1st Qu. :0.03015 Median :0.04809 Mean :0.06200 3 rd Qu . : 0 . 0 7 6 6 6 !'-lax . : 0 . 2 55 52 includes transaction ID lists: FALSE mining info: data ntransactions support confidence Groceries 9835 0.02 1 r·1ax. 1 The following code uses the inspect () function to display the top 10 frequent 1-itemsets sorted by their support. Of all the transaction records, the 59 1-itemsets such as {whole milk}, {other vegetables}, {rolls/buns}, {soda}, and {yogurt} allsatisfytheminimum support. Therefore, they are called frequent 1-itemsets. inspect(head(sort(iternsets, by = "support 11 ), 10)) items support 1 {whole milk} 0.25551601 2 {other vegetables} 0.19349263 3 {rolls/buns} 0.18393493 4 {soda} 0.17437722 5 {yogurt} 0.13950178 6 {bottled water} 0.11052364 ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES 7 {root vegetables} 8 {tropical fruit} 9 {shopping bags} 10 {sausagef 0.10899847 0.10.J93137 0.09852567 0.09395018 In the next iteration, the list of frequent 1-itemsets is joined onto itself to form all possible candidate 2-itemsets. For example, 1-itemsets {who 1 e milk} and { soda} would be joined to become a 2-itemset {whole milk, soda}. The algorithm computes the support of each candidate 2-itemset and retains those that satisfy the minimum support. The output that follows shows that 61 frequent 2-itemsets have been identified. itemsets <- apriori(Groceries, parameter~list(minlen=2, maxlen=2, support=0.02, target="frequent itemsets")) parameter specification: confidence minval smax arem aval originalSupport support minlen 0.8 0.1 l 1:one ?i\LSE TRUE 0.02 2 ma:den target ext 2 frequent itemsets FALSE algorithmic control: filter tree heap memopt load sort verbose 0.1 TRUE TRUE FALSE TRUE 2 TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt set item appear.:1nces ... [0 item(s)] done [O.OOs]. set transaction:; ... [169 item(s), 9835 transaction(s;] done [O.OOs). sorting and receding item.s ... [59 item(s)] done [O.OOs]. creating transaction tree ... done [O.OOs]. checking subsets of size l 2 done [O.OOs]. \•niting ... [61 set:s)] clone [O.OOs]. Cl·eating S4 object ... done [O.OOs]. The summary of the itemsets shows that the support of 2-itemsets ranges from 0.02003 to 0.07483. summary(itemsets) set of 61 itemsets most frequent items: whole mil~ other vegetables 25 :7 soda S3 yogurt 9 element (itemset•tra~sactlon length distribution:si:es 2 61 rolls/buns 9 Min. 1st Qu. Median !•lean 3 rd Qu. 2 2 2 summary of quality measures: support l·l in . : J . 0 2 0 0 3 1st Qu. :0.02227 !·ledian : 0. 02613 !·lean :0.02951 3rd Qu. :0.03223 !•lax. :0.07483 2 includes transaction ID lists: FALSE mining info: 2 data ntransactions support confidence Groceries 9835 0.02 5.5 An Example: Transactions in a Grocery Store 2 The top 10 most frequent 2-itemsets are displayed next, sorted by their support. Notice that whole milk appears six times in the top 10 2-itemsets ranked by support. As seen earlier, {whole milk} has the highest support among all the 1-itemsets. These top 10 2-itemsets with the highest support may not be interesting; this highlights the limitations of using support alone. inspect(head(sort(itemsets, by ="support 11 ),10)) items support {other vegetables, >·:hole milk} 0.07..;83477
2 {·.·:hole milk,
rolls/buns} 0.05663-i47
{whole milk,
yogurt} 0.056024-iO
{root vegetables,
whole milk} 0.04890696
5 {root vegetables,
other vegetables} 0.04738180
6 {other vegetables,
yogurt} 0.04341637
7 {other vegetables,
rolls/buns} 0.04260295
8 {tropical fruit,
\•Jhole milk} 0.04229792
9 { vlhole milk,
soda} 0.04006101
10 {rolls/buns,
soda} 0.03833249
Next, the list of frequent 2-itemsets is joined onto itself to form candidate 3-itemsets. For example
{other vegetables, whole milk} and {whole milk, rolls/buns} wouldbejoined
as {other vegetables, whole milk, rolls/buns}. The algorithm retains those itemsets

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
that satisfy the minimum support. The following output shows that only two frequent 3-itemsets have
been identified.
itemsets <- apriori(Groceries, parameter~list(minlen=3, maxlen~3, support~0.02, target~"frequent itemsets")) parameter specification: confidence minval smax arem aval originalSupport support minlen 0.8 0.1 1 none FALSE TRUE 0.02 max len target ext frequent itemsets FALSE algorithmic control: filter tree heap memopt load sort verbose 0.1 TRUE TRUE FALSE TRUE 2 TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt set item appearances ... [0 item(s)] done [O.OOs]. set transactions ... [169 item(s), 9835 transaction(s)] done [O.OOs]. sorting and receding items ... [59 item(s)) done (O.OOs]. creating transaction tree ... done [0. OOs) . checking subsets of size 1 2 3 done [O.OOs]. writing . . . [2 set (s)] done [O. OOs]. creating S4 object ... done (O.OOs]. The 3-itemsets are displayed next: inspect(sort(itemsets, by ="support")) items support 1 {root vegetables, other vegetables, whole milk} 0.02318251 2 {other vegetables, whole milk, yogurt} 0.02226741 In the next iteration, there is only one candidate 4-itemset {root vegetables, other vegetables, whole milk, yogurt}, and its support is below 0.02. No frequent 4-itemsets have been found, and the algorithm converges. itemsets <- apriori(Groceries, parameter~list(minlen~4, maxlen=4, support=0.02, target~"frequent itemsets")) parameter specification: confidence minval smax arem aval origi~alSupport support minlen 0.8 0.1 1 none FALSE TRUE 0.02 4 max len target ext frequent itemsets FALSE 5.5 An Example: Transactions in a Grocery Store algorithmic control: filter tree heap memopt load sort verbose 0.1 TRUE TRUE FALSE TRUE 2 TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt set item appearances ... [0 item(s)] done [O.OOs]. set transactions ... [169 item(s), 9835 transaction(s)] done [O.OOs]. sorting and receding items . . . [59 item (s)] done [0. OOs] . creating transaction tree ... done [O.OOs]. checking subsets of size 1 2 3 done [O.OOs]. writing ... [0 set(s)] done [O.OOs]. creating S4 object ... done [0. OOs] . The previous steps simulate the Apriori algorithm at each iteration. For the Groceries dataset, the iterations run out of support when k = 4. Therefore, the frequent itemsets contain 59 frequent 1-itemsets,61 frequent 2-itemsets, and 2 frequent 3-itemsets. When the max len parameter is not set, the algorithm continues each iteration until it runs out of support or until k reaches the default maxlen=l o. As shown in the code output that follows, 122 frequent itemsets have been identified. This matches the total number of 59 frequent 1-itemsets, 61 frequent 2-itemsets, and 2 frequent 3-itemsets. itemsets <- apriori(Groceries, parameter=list(minlen=l, support=0.02, target= 11 frequent itemsets 11 )) parameter specification: confidence minval smax arem aval originalSupport support minlen 0.8 0.1 1 none FALSE TRUE 0.02 max len target ext 10 frequent itemsets FALSE algorithoic control: filter tree heap memopt load sort verbose 0.1 ~RUE TRUE FALSE TRUE TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09! (c) 1996-2004 Christian Borgelt set item appearances ... [0 items!] dOJ:e [o.oos:. set transactions ... [169 item(s, 9835 transaction(s)) done [O.OOs). sorting and receding items ... :s9 item(s!] done [O.OOs]. creating transaction tree ... done [O.OOs]. checking subsets of size 1 2 3 done [O.OOs). ,,.;riting ... [122 setl,s)] done [O.OOs]. creating S.f object ... done [O.OOs]. Note that the results are assessed based on the specific business context of the exercise using the specific dataset. If the dataset changes or a different minimum support threshold is chosen, the Apriori algorithm must run each iteration again to retrieve the updated frequent itemsets. ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES 5.5.3 Rule Generation and Visualization The apriori () function can also be used to generate rules. Assume that the minimum support threshold is now set to a lower value 0.001, and the minimum confidence threshold is set to 0.6. A lower minimum support threshold allows more rules to show up. The following code creates 2,918 rules from all the transac- tions in the Groceries dataset that satisfy both the minimum support and the minimum confidence. rules <- apriori(Groceries, parameter=list(support=O.OOl, confidence=0.6, target= 11 rules 11 )) parameter specification: confidence minval smax arem aval originalSupport support minlen 0.6 0.1 1 none FALSE TRUE 0.001 1 maxlen target ext 10 rules FALSE algorithmic control: filter tree heap memopt load sort verbose 0.1 TRUE TRUE FALSE TRUE 2 TRUE apriori - find association rules with the apriori algorithm version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt set item appearances ... (0 item(s)] done (O.OOs]. set transactions ... [169 item{s), 9835 transaction(s)] done (O.OOs]. sorting and receding items . . . [157 item (s) J done [0. OOs]. creating transaction tree ... done [O.OOs]. checking subsets of size 1 2 3 4 5 6 done [0.01s]. writing ... [2918 rule(s)] done [O.OOs]. creating S4 object ... done [0.01s]. The summary of the rules shows the number of rules and ranges of the support, confidence, and lift. summary(rules) set of 2918 rules rule length distribution {lhs + rhs) :sizes 2 3 4 5 6 3 490 1765 626 34 l\1in. 1st Qu. 2.000 4.000 l·ledian 4.000 1-lean 3 rd Qu. 4.068 4.000 l\lax. 6.000 summary of quality measures: support confidence Min. :0.001017 Min. :0.6000 1st Qu. :0.001118 1st Qu. :0.6316 l\ledian :0. 001220 Mean :0.001480 3rd Qu. :0.001525 !>lax. :0.009354
l>’Jedian :0.6818
!•lean :0.7028
3rd Qu.: 0. 7500
Max . : 1 . 0 0 0 0
lift
1-lin. 2.348
1st Qu.: 2. 668
r-ledian : 3 . 16 8
!\lean 3.450
3rd Qu.: 3.692
!·lax. : 18. 996

5.5 An Example: Transactions in a Grocery Store
min1n 1 info:
d3•1 ntt n .. f l n
Enter p lot (rules) to display the scatterplot of the 2,918 rules (Figure 5-3), where the horizontal
axis is the support, the vertical axis is the confidence, and the shading is the lift. The scatterplot shows
that, of the 2,918 rules generated from the Groceries dataset, the highest lift occu rs at a low support
and a low confidence.
Scatter plot for 2918 rules
0 9 15
Q)
u
c
Q)
“0 0 8
~
0
10
u
07
5
0 6
0.002 0 004 0.006 0.008
lift
support
FIGURE 5-3 Scatterplot of t he 2,978 rules with minimum support 0.007 and minimum confidence 0.6
Entering p lot ( rules®q u al i ty) displays a scatterplot matrix (Fig ure 5-4) to compare the sup-
port, confidence, and lift of the 2,9 18 rules.
Figure 5-4 shows that lift is propor tional to confidence and illustrates several linear groupings. As
indicated by Equation 5-2 and Equation 5-3, Lift = Confidence / Support(Y). Therefore, when the support
of Y remains the same, lift is proportional to confidence, and the slope of the linear trend is the recipro-
cal of Support(Y). The following code shows that, of the 2,918 rules, there are only 18 different values for
– –
1
– , and the majority occurs at slopes 3.91, 5.17, 7.17, 9.17, and 9.53. This matches the slopes shown
Support(Y)
in the third row and second column of Figure S-4, where the x-axis is the confidence and the y-axis is the lift.
;; comput • Llw l/Suppot·t ry
slo pe<· sort(round (rules®quality$lift I r ul es®quality$con f ide nce , 2)) = D .... sp .... 3.Y ,.. ht 1 Jtiti 1 ~ • j 1 h ~ t! ~ .1 la t :i~ ... unlist( l apply(spl it(s l o pe,f =slope) ,length ) ) ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES 0 "' ci cq 0 ..... ci c:Jb coo 0
I
0.002 0.004 0.006 0.008 5 10
FIGURE 5-4 Scatterplot matrix on the support, confidence, and lift of the 2,918 rules
The inspect () fun ction can display content of the rules generated previously.
The following code shows the top ten rules sorted by the lift. Ru le {Instant food
p rodu cts , soda} …. ( hamburger meat} has the highest lift of 18.995654.
inspec t(head(sort(rules , by=”lift”) , 10))
rrturg r <>a· I
0.
3 {ham
0.
CD
0
0
ci
“‘ 0 0 0
0 0 ci
0 0 0
~
~
l{)
15

4 {tropical fruit,
other vegetables,
yogurt,
white bread} => {butter}
0.001016777 0.6666667 12.030581
5 {hamburger meat,
yogurt,
whipped/sour cream} => {butter}
0.001016777 0.6250000 11.278670
6 {tropical fruit,
other vegetables,
whole milk,
yogurt,
domestic eggs} => {butter}
0.001016777 0.6250000 11.278670
7 {liquor,
red/blush wine} => {bottled beer}
0.001931876 0.9047619 11.235269
8 {other vegetables,
5.5 An Example: Transactions in a Grocery Store
butter,
sugar}
0.001016777 0.7142857
9 {whole milk,
butter,
=> {whipped/sour cream}
9.964539
hard cheese}
0.001423488 0.6666667
10 {tropical fruit,
other vegetables,
butter,
=> {whipped/sour cream}
9.300236
fruit/vegetable juice} => {whipped/sour cream}
0.001016777 0.6666667 9.300236
The following code fetches a total of 127 rules whose confidence is above 0.9:
confidentRules <- rules[quality(rules)$confidence > 0.9]
confidentRules
set of 127 rules
The next command produces a matrix-based visualization (Figure 5-5) of the LHS versus the RHS of
the rules. The legend on the right is a color matrix indicating the lift and the confidence to which each
square in the main matrix corresponds.
plot(confidentRules, method= 11 matrix 11 , measure=c( 11 lift 11 , “confidence 11 ),
control=list(reorder=TRUE))
As the previous plot () command runs, the R console would simultaneously display a distinct list of
the LHS and RHS from the 127 rules. A segment of the output is shown here:
Itemsets in Antecedent (LHS)
[1] “{citrus fruit,other vegetables,soda,fruit/vegetable juice}”
[2] “{tropical fruit,other vegetables,whole milk,yogurt,oil}”

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
[3] ” (trop ical fnti t, butter, whipped/sour cream, frult/vegetable
juice) ”
[4] ” { t1·opical fruit , g1·apes, •,:hole milk, yogu~:t)”
[5] “{ham,trop ical auit,pip fruit,whole mllk}”
[124] ” {liquar,rec/blush ‘”‘lne}”
Itemsets in Consequent (RHS)
[l] “{whole mllk}” “(rogurt} ” “{root vegetables} ”
[ •l ” {not tled beet}” ” {other vegetables}”
Matrix with 127 rules
5
Cii 4
I
Q;_
c
Q) 3 ~
0″
Q)
“‘ c
0
0 2
20 40 60 80 100 120
Antecedent (LHS)
10
~ 8
6
4
F IGURE 5-5 Matrix-based visualization of LHS and RHS, colored by lift and confidence
0.92 0.96
confidence
Th e follo w in g code provides a visual ization of the top five r ul es with the highest lift. The plot
is shown in Figure S-6. In the graph, the arrow always poi nts from an item on the LHS to an it em on
the RHS. For example, the arrows that connect ham, proce ssed cheese, and whi te bread suggest rule
{h a m, p rocessed cheese} -> {wh i te b rea d } . The legend on the top ri ght of t he graph
shows that the size of a circle indicates the support of the rules ranging from 0.001 to 0.002. The color
(or shade} represents th e li ft, whic h ranges from 11.279 to 18.996. The ru le w ith the high es t lift is
{Inst a n t food produ cts , soda} -> {h a mbu r ger me a t} .
h ighLi ftRule s c- hea d(sort (rules, by= ” lift ” ) , 5)
p lot(highLiftRules , method =” graph” , cont r ol=lis t (type=”items” ))

5 .6 Va lidation a nd Testing
Graph for 5 rules
size: support (0. 001 · 0. 002)
color: lift (11.279 -1 8.996)
tropical fruit
other vegetables
processed cheese
ham
popcorn
t
salty snack
wgite bread
soda
FIGURE 5-6 Graph visualization of the top five rules sorted by lift
5.6 Validation and Testing
yogurt
……
butter
1
~—-whipped/sour cream
hamburger meat
~
Instant food products
After gathering the output rules, it may become necessary to use one or more methods to validate the
results in the business context for the sample dataset. The first approach can be established through
statistical measures such as confidence, lift, and leverage. Rules that involve mutua lly independent items
or cover few transactions are considered uninteresting because they may capture spurious relationships.
As mentioned in Section 5.3, confidence measures the chance that X andY appear together in rela-
tion to the chance X appears. Confidence can be used to identify the interestingness of the ru les.
Lift and leverage both compare the support of X andY against their individual support. While min-
ing data with association rul es, some rul es generated cou ld be purely coincidenta l. For example, if 95%
of customers buy X and 90% of customers buy Y, then X and Y would occur together at least 85% of the
time, even if there is no relationship between the two. Measures like lift and leverage ensure that inter-
esting rul es are identified rather than coincidental ones.
Another set of criteri a can be established through subjective arguments. Even with a high confidence,
a rule may be considered subjectively uninteresting unless it reveals any unexpected profi table actions.
For example, ru les like {paper}-+ { penci 1} may not be subj ectively interesting or mean ingful despite
high support and confidence values. In contrast, a rule like {diaper}->{ b eer } that satisfies both mini-
mum support and minimum confidence can be considered subjectively interesting because this rule is

ADVA NCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULES
unexpected and may suggest a cross-sell opportun ity for the retailer. This incorporation of subjective
knowledge into the evaluation of rules can be a difficult task, and it requires collaboration with domain
experts. As seen in Chapter 2, “Data Ana lytics Lifecycle,” the domain experts may serve as the business
users or the business intelligence analysts as part of the Data Science team. In Phase 5, the team can com-
municate the results and decide if it is appropriate to operationalize them.
5.7 Diagnostics
Although the Apriori algori thm is easy to understand and implement, some of the rules generated are
uninteresting or practica lly useless. Additionally, some of the rules may be generated due to coincidental
relationships between the variables. Measures like confidence, lift, and leverage should be used along with
human insights to address this problem.
Another problem with association rules is that, in Phase 3 and 4 of the Data Analytics Lifecycle
(Chapter 2), the team must specify the minimum support prior to the model execution, which may lead
to too many or too few rules. In rela ted research, a va riant of the algorithm [13] can use a predefined
target range for the number of rules so that the algorithm can adjust the minimum support accordingly.
Section 5.2 presented the Apriori algorithm, which is one of t he earliest and the most fundame ntal
algorithms for generating association rules. The A priori algorithm reduces the computational workload by
only examining itemsets that meet the specified minimum threshold. However, depending on the size of the
dataset, the Apriori algorithm can be computationa lly expensive. For each level of support, the algorithm
requires a scan of the entire database to obtain the result. Accordingly, as the database grows, it takes more
time to compute in each run. Here are some approaches to improve Apriori’s efficiency:
• Part itioning: Any item set that is potential ly frequent in a transaction database must be frequent in
at least one of the partitions of the transaction database.
• Sa mpli ng: This extracts a subset of the data with a lower support threshold and uses the subset to
perform association rule mining.
• Transaction reduction: A transaction that does not contain frequen t k-itemsets is useless in sub-
sequent scans and therefore can be ignored.
• Hash- based item set cou nting : If the corresponding hash ing bucket count of a k-itemset is
below a certain threshold, the k-itemset cannot be frequent.
• Dynamic itemset counting: Only add new candidate itemsets when all of their subsets are esti-
mated to be frequent.
Summary
As an unsupervised ana lysis technique that uncovers relationships among items, association rules find many
uses in activities, includ ing market basket analysis, clickstream analysis, and recommendation engines.
Although association ru les are not used to predict outcomes or behaviors, they are good at identifying
“interesting” relationships within items from a la rge dataset. Quite often, the disclosed relationships that
the association rules suggest do not seem obvious; they, therefore, provide va luab le insights for institutions
to improve their business operations.

Exercises
The Apriori algorithm is one of the earliest and most fundamental algorithms for association rules. This
chapter used a grocery store example to walk through the steps of A priori and generate frequent k-itemsets
and useful rules for downstream analysis and visualization. A few measures such as support, confidence,
lift, and leverage were discussed. These measures together help identify the interesting ru les and elimi-
nate the coincidental rules. Finally, the chapter discussed some pros and cons of the Apriori algorithm and
highlig hted a few methods to improve its efficiency.
Exercises
1. What is the Apriori property?
2. Following is a list of five transactions that include items A, B, C, and D:
• Tl : { A,B,C
• T2: { A,C }
• T3: { B,C }
• T4: { A,D }
• TS: { A,C,D
Which itemsets satisfy the minimum support of 0.5? (Hint: An item set may include more
than one item.)
3. How are interesting rules identified? How are interesting rul es distinguished from coincidental rules?
4. A local retailer has a database that stores 10,000 transactions of last summer. After analyzing the data,
a data science team has identified the following statistics:
• { ba ttery} appears in 6,000 transactions.
• {sunscreen} appears in 5,000 transactions.
• {sandals} appears in 4,000 transactions.
• {bowls} appears in 2,000 transactions.
• {battery, sunscreen} appears in 1,500 transactions.
• {battery, sandals} appears in 1,000 transactions.
• {battery, bowls} appears in 250 transactions.
• {battery, sunscreen, sandals} appears in 600 tra nsactions.
Answer the following questions:
a. What are the support values of the preceding itemsets?
b. Assuming the minimum support is 0.05, which itemsets are considered frequent?
c. What are the confidence values of {battery}-> { sunscreen} and
{battery, sunscreen}->{ sandals} ? Which of the two rules is more interesting?
d. List all the candidate rules that can be formed from the statistics. Which rules are cons id ered
interesting at the minimum confidence 0.25? Out of these interesting rul es, which rule is con-
sidered the most useful (that is, least coincidental)?

ADVANCED ANALYTICAL THEORY AND METHODS: ASSOCIATION RULE S
Bibliography
[1] P. Hajek, I. Havel, and M. Chytil, “The GUHA Method of Automatic Hypotheses Determination,”
Computing, vol. 1, no. 4, pp. 293-308, 1966.
[2] R. Agrawal, T. lmielinski, and A. Swami, “Mining Association Ru les Between Sets of Items in Large
Databases,” SIGMOD ’93 Proceedings of the 1993 ACM SIGMOD International Conference on
Management of Data, pp. 207-216,1993.
[3] M.-S. Chen, J. S. Park, and P. Yu, “Efficient Data Mi ni ng for Path Traversal Patterns,” IEEE Transactions
on Knowledge and Data Engineering, vol. 10, no. 2, pp. 209-221, 1998.
[4] R. Cooley, B. Mobasher, and J. Srivastava, “Web Mining: Information and Pattern Discovery on the
World Wide Web,” Proceedings of the 9th IEEE International Conference on Tools with Artificial
Intelligence, pp. 558-567, 1997.
[5] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” in
Proceedings of the 20th International Conference an Very Large Data Bases, San Francisco, CA,
USA, 1994.
[6] S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, “Dynamic ltemset Counting and Implication Rules for
Market Basket Data,” SIGMOD, vol. 26, no. 2, pp. 255-264, 1997.
[7] G. Piatetsky-Shapiro, “Discovery, Analysis and Presentation of Strong Rules,” Knowledge Discovery in
Databases, pp. 229-248, 1991.
[8] S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Baskets: Generalizing Association Rules to
Correlations,” Proceedings of the ACM SIGMOD/PODS ’97 Joint Conference, vol. 26, no. 2, pp. 265-
276, 1997.
[9] C. C. Aggarwal and P. S. Yu, “A New Framework for ltemset Generation,” in Proceedings of the
Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS
’98), Seattle, Washington, USA, 1998.
[1 O] M. Hahsler, “A Comparison of Commonly Used Interest Measures for Association Rules,” 9 March
2011. [Online). Available: http: I / michael. hahsler . net / research /
association_rules / measures . html. [Accessed 4 March 2014).
[11] W. Lin, S. A. Alvarez, and C. Ruiz, “Efficient Adaptive-Support Association Rule Mining for
Recommender Systems,” Data Mining and Knowledge Discovery, vol. 6, no. 1, pp. 83-105,2002.
[12] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, “Effective Personalization Based on Association Rule
Discovery from Web Usage Data,” in ACM, 2011.
[13] W. Lin, S. A. Alvarez, and C. Ruiz, “Collaborative Recommendation via Adaptive Association
Rule Mining,” in Proceedings of the International Workshop on Web Mining forE-Commerce
(WEBKDD), Boston, MA, 2000.

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
In general, regression analysis attempts to explain the influence that a set of variables has on the outcome of
another variable of interest. Often, the outcome variable is called a dependent variable because the out-
come depends on the other variables. These additional variables are sometimes called the input variables
or the independent variables. Regression analysis is useful for answering the following kinds of questions:
• What is a person’s expected income?
• What is the probability that an applicant will default on a loan?
Linea r regression is a useful tool for answering the first question, and logistic reg ression is a popular
method for add ressing the second. This chapter exam ines the se two reg ression techniq ues and explains
when one technique is more appropriate than the other.
Regression analysis is a useful explanatory tool that can identify the input va riables that have the great-
est statistica l infl uence on the outcome. With such knowledge and insight, environmental changes can
be attempted to produce more favorable values of the input variables. For example, if it is found that the
reading level of 10-year-old students is an excellent predictor of the students’ success in high school and
a factor in their attending college, then additional emphasis on reading can be considered, implemented,
and evaluated to improve students’ reading levels at a younger age.
6.1 Linear Regression
Linear regression is an analytical technique used to model the relationship between several input variables
and a continuous outcome variable. A key assumption is that the relationsh ip between an input va riable
and the outcome variable is linear. Although this assumption may appear restrictive, it is often possible to
properly transform the input or outcome variables to achieve a li near relationship between the modified
input and outcome variables. Possible transformations will be covered in more detail later in the chapter.
The physical sciences have we ll-known linear models, such as Ohm ‘s Law, which states t hat th e electri-
ca l current flowing through a resistive circuit is linearly proportional to the voltage applied to the circuit.
Such a model is considered deterministic in the sense that if the input values are known, the value of the
outcome va ri able is precisely determined. A linea r regression model is a probabilistic one that accounts for
the randomness that can affect any particular outcome. Based on known input val ues, a linear regression
model provides the expected value of the outcome va riable based on the values of the input variables,
but some uncertainty may remain in predicting any particular outcome. Thus, linear regression models are
useful in physical and social science applications where there may be considerable variation in a particular
outcome based on a given set of input values. After presenting possible linear regression use cases, the
fou ndations of linear regression modeling are provided.
6.1.1 Use Cases
Linear regression is often used in business, government, and other scenarios. Some common practical
applications of linear regression in the real worl d inc lude the following:
• Rea l estate: A simple linear regression analysis can be used to model residential home prices as a
function of the home’s living area. Such a model helps set or evaluate the list price of a home on the
market. The model could be further improved by including other input variables such as number of
bathrooms, number of bed room s, lot size, school district ran kings, crime statistics, and property taxes.

6.1 Linear Regression
o Demand forecasting: Businesses and governments can use linear regression models to predict
demand for goods and services. For example, restaurant chains can appropriately prepare for
the predicted type and quantity of food that customers will consume based upon the weather,
the day of the week, whether an item is offered as a special, the time of day, and the reservation
volume. Similar models can be built to predict retail sales, emergency room visits, and ambulance
dispatches.
o Medical: A linear regression model can be used to analyze the effect of a proposed radiation treat-
ment on reducing tumor sizes. Input variables might include duration of a single radiation treatment,
frequency of radiation treatment, and patient attributes such as age or weight.
6.1.2 Model Description
As the name of this technique suggests, the linear regression model assumes that there is a linear relation-
ship between the input variables and the outcome variable. This relationship can be expressed as shown
in Equation 6-1.
where:
y is the outcome variable
xi are the input variables, for j = 1, 2, .. . , p- 1
{3
0
is the value of y when each xi equals zero
f]i is the change in y based on a unit change in xi’ for j = 1, 2, .. • , p- 1
Eisa random error term that represents the difference in the linear model and a particular
observed value for y
(6-1)
Suppose it is desired to build a linear regression model that estimates a person’s annual income as a
function of two variables-age and education-both expressed in years. In this case, income is the outcome
variable, and the input variables are age and education. Although it may be an over generalization, such
a model seems intuitively correct in the sense that people’s income should increase as their skill set and
experience expand with age. Also, the employment opportunities and starting salaries would be expected
to be greater for those who have attained more education.
However, it is also obvious that there is considerable variation in income levels for a group of people with
identical ages and years of education. This variation is represented byE in the model. So, in this example,
the model would be expressed as shown in Equation 6-2.
(6-2)
In the linear model, the pis represent the unknown p parameters. The estimates for these unknown
parameters are chosen so that, on average, the model provides a reasonable estimate of a person’s income
based on age and education. In other words, the fitted model should minimize the overall error between
the linear model and the actual observations. Ordinary least Squares (OLS) is a common technique to
estimate the parameters.
To illustrate how OLS works, suppose there is only one input variable, x, for an outcome variable y.
Furthermore, n observations of { x, y) are obtained and plotted in Figure 6-1.

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
II)
(“)
0
0
0
0 (“)
II’) 0
N
>- 0 0 N 0
0 0
II)
0
II) 0
2 4 6 8 10 12
X
F1GURE6- Scatcerplot of y versus x
The goal is to find the line that best approximates the relation ship between the outcome va riable
and th e input variables . With OLS, th e objec ti ve is to find the line through these points tha t mini-
mizes the sum of the squa res of the difference b etween each point and the line in the ver tical direc-
tion. In other words, find the values of -1
0
and .-1, such that the su mmation shown in Equat ion 6-3 is
minimized.
(6-3)
The n individual distances to be squa red and then summ ed are illustrated in Figure 6-2. The vertical
lines represent the distance between each observed y value and the line y = i
0
;- i,x.
II)
(“)
0
(“)
II)
N
>- 0
N
II)
0
II)
2 4 6 8 10 12
X
FIGURE 6 2 Scatterplot of y versus x with vertical distances from th e observed points to a fitted line

6.1 Linear Regression
In Figure 3-7 of Chapter 3, “Review of Basic Data Analytic Methods Using R,” the Anscombe’s Quartet
example used OLS to fit the linear regression line to each of the four data sets. OLS for multiple input vari-
ables is a straightforward extension of the one input variable case provided in Equation 6-3.
The preceding discussion provided the approach to find the best linear fit to a set of observations.
However, by making some additional assumptions on the error term, it is possible to provide further capa-
bilities in utilizing the linear regression model. In general, these assumptions are almost always made, so
the following model, built upon the earlier described model, is simply called the linear regression model.
Linear Regression Model (with Normally Distributed Errors)
In the previous model description, there were no assumptions made about the error term; no additional
assumptions were necessary for OLS to provide estimates of the model parameters. However, in most
linear regression analyses, it is common to assume that the error term is a normally distributed random
variable with mean equal to zero and constant variance. Thus, the linear regression model is expressed as
shown in Equation 6-4.
Y = f3o + fJ1X1 + fJ2X2 •• .+ f3P-1xp- 1 +E
where:
y is the outcome variable
xi are the input variables, for j = 1, 2, … , p- 1
{3
0
is the value of y when each xi equals zero
{3i is the change in y based on a unit change in xi’ for j = 1, 2, … , p -1
E- N(O, U
2
) and the ES are independent of each other
(6-4)
This additional assumption yields the following result about the expected value of y, E(y) for given
(x
1
,x2, … xP_ 1):
E(y) = E(/30 + (31x 1 + {32×2 . .. + f)P_ 1xP_ 1 +c)
= !3o + !31×1 + j32x2 •.. + {3p-1xp-1 + E(c)
= {30 + ~j1 x1 + /32×2 ••• + f3P_ 1XP_ 1
Because {3. and x .are constants, the E(y) is the value of the linear regression model for the given
J I
(x
1
,x
2
, •• • xP_ 1). Furthermore, the variance ofy, V(y), for given (x 1,×2, ••• xP _1) is this:
V(y) = V(f3o + (31×1 + /32×2 … + !3p-1xp-1 +e)
=0+V(c)=a 2
Thus, for a given (x1,x2, . .. xP _1), y is normally distributed with mean /30 + /31×1 + ;32x 2 … + !3P _1xP_ 1
and variance a 2• For a regression model with just one input variable, Figure 6-3 illustrates the normality
assumption on the error terms and the effect on the outcome variable, y, for a given value of x.
For x = 8, one would expect to observe a value of y near 20, but a value of y from 15 to 25 would appear
possible based on the illustrated normal distribution. Thus, the regression model estimates the expected
value of y for the given value of x. Additionally, the normality assumption on the error term provides some
useful properties that can be utilized in performing hypothesis testing on the linear regression model and

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
providing confidence intervals on the parameters and the mean ofy given(x1,x2 , … xP_,). The application of
these statistical techniques is demonstrated by applying R to the earlier linear regression model on income.
10
(“)
0
(“)
10
N
>. 0 N
10
0
10
2 4 6
X
FIGURE 6-3 Normal distribution about y for a given value of x
ExampleinR
8 10 12
Returning to the Income example, in addition to the variables age and education, the person’s gender,
female or male, is considered an input variable. The following code reads a comma-separated-value (CSV)
file of 1,500 people’s incomes, ages, years of education, and gender. The first 10 rows are displayed:
income_input = as.data.frame( read. csv ( 11 C: /data/income. csv”)
income_input[l:lO,]
ID Income Age Education Gender
1 1 113 69 12 1
2 2 91 52 18 0
121 65 14 0
4 4 81 58 12 0
5 5 68 31 16 1
6 6 92 51 15 1
7 7 75 53 15 0
8 8 76 56 13 0
9 9 56 42 15
10 10 53 33 11 1
Each person in the sample has been assigned an identification number, ID. Income is expressed in
thousands of dollars. (For example, 113 denotes $113,000.) As described earlier, Age and Education are
expressed in years. For Gender, a 0 denotes female and a 1 denotes male. A summary of the imported
data reveals that the incomes vary from $14,000 to $134,000. The ages are between 18 and 70 years. The
education experience for each person varies from a minimum of 10 years to a maximum of 20 years.
summary(income_input)
ID Income Age Education
f>’lin. 1.0 !>lin. : 14.00 !>lin. :18.00 Min. :10.00

6.1 linear Regression
t Qu .
‘1 X
As described in Cha pter 3, a scatterplot matrix is an informative tool to view the pair-wise relationships
of the va ri ables. The basic assumption of a linear regression model is that there is a linear relationship
between the outcome variable and the input variables. Using the lattice package in R, the scatterplot
matri x in Figure 6-4 is generated with the following R code:
co . OQOOt.:.•OQOOOO
Gender
0 OOOO t~t~OOOOO
0 ·~..O:’C .:K1D t~a=-:-mOEll> 0 0
·-· ••Ill) C8111:111D~-~-= 0 0 ,;:a-••wllq, CD CICI!miDCilltmlll:llllro 0 0
« Ill! »c:::-=:ra::D~ G:II::C 0 0
0 0 0
c.:::o:,.~_.I::::.::t~’21C Educabon 0 ‘J
-==-=-=-=-=~.–s::a::. 0 0
0 0
~ £-~
Income -~ – ~ ~~~- –~-~ … ~- ,· ..
. ‘” -:~ … .. …
Scatter Plol l.lalnx
FIGURE 6-4 Scatterplot matrix of the variables

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
library(lattice)
splom(-income_input[c(2:5)], groups~NULL, data=income_input,
axis.line.tck = 0,
axis.text.alpha ~ 0)
Because the dependent variable is typically plotted along they-axis, examine the set of scatterplots
along the bottom of the matrix. A strong positive linear trend is observed for Income as a function of Age.
Against Education, a slight positive trend may exist, but the trend is not quite as obvious as is the case
with the Age variable. Lastly, there is no observed effect on Income based on Gender.
With this qualitative understanding of the relationships between Income and the input variables, it
seems reasonable to quantitatively evaluate the linear relationships of these variables. Utilizing the normal-
ity assumption applied to the error term, the proposed linear regression model is shown in Equation 6-5.
Income= {3
0
+ {31Age + !3iducation + {33Gender + E (6-5)
Using the linear model function, lm (),in R, the income model can be applied to the data as follows:
results <- lm(Income-Age + Education + Gender, income_input) summary(results) Call: lm(fornula Income - Age + Education ~ Gender, data incorne_input) Residuals: Min 1Q Median 3Q Max -37.340 -8.101 0.139 7.885 37.271 Coefficients: Estimate Std. Srro!.~ t value Pl.~ (>It i )
(Intercept) 7.26299 1.95575 3.714 0.000212
.~ge 0.99520 0.02057 48.373 < 2e-l6 Education 1.75788 0.11581 15.179 < 2e-16 Gender -0.93433 0.62388 -1.498 0.134443 Signif. codes: 0 '***' 0.001 '**' 0.01 '* 1 0.05 1 • 1 0.1 1 1 1 Residual standard error: 12.07 on 1496 degrees of freedom Multiple R-squared: 0.6364, Adjusted R-squared: 0.6357 F-statistic: 873 on 3 and 1496 DF, p-value: < 2.2e-16 The intercept term, {3 0 , is implicitly included in the model. The lm {) function performs the parameter estimation for the parameters {3i (j = 0, 1, 2, 3) using ordinary least squares and provides several useful calculations and results that are stored in the variable called results in this example. After the stated call to 1m ( } , a few statistics on the residuals are displayed in the output. The residuals are the observed values of the error term for each of then observations and are defined fori= 1, 2, ... n, as shown in Equation 6-6. e; = Y; -(bo +blxi,l +b2xi,2 ... +bp-1xi,p-1) where bi denotes the estimate for parameter {3i for j = 0, 1, 2, ... p - 1 (6-6) 6.1 Linear Regression From the R output, the residuals vary from approximately -37 to +37, with a median close to 0. Recall that the residuals are assumed to be normally distributed with a mean near zero and a constant variance. The normality assumption is examined more carefully later. The output provides details about the coefficients. The column Estimate provides the OLS estimates of the coefficients in the fitted linear regression model. In general, the (Intercept) corresponds to the estimated response variable when all the input variables equal zero. In this example, the intercept cor- responds to an estimated income of $7,263 for a newborn female with no education. It is important to note that the available dataset does not include such a person. The minimum age and education in the dataset are 18 and 10 years, respectively. Thus, misleading results may be obtained when using a linear regression model to estimate outcomes for input values not representative within the dataset used to train the model. The coefficient for Age is approximately equal to one. This coefficient is interpreted as follows: For every one unit increase in a person's age, the person's income is expected to increase by $995. Similarly, for every unit increase in a person's years of education, the person's income is expected to increase by about $1,758. Interpreting the Gender coefficient is slightly different. When Gender is equal to zero, the Gender coefficient contributes nothing to the estimate of the expected income. When Gender is equal to one, the expected Income is decreased by about $934. Because the coefficient values are only estimates based on the observed incomes in the sample, there is some uncertainty or sampling error for the coefficient estimates. The Std. Error column next to the coefficients provides the sampling error associated with each coefficient and can be used to perform a hypothesis test, using the t-distribution, to determine if each coefficient is statistically different from zero. In other words, if a coefficient is not statistically different from zero, the coefficient and the associ- ated variable in the model should be excluded from the model. In this example, the associated hypothesis tests' p-values, Pr (>It I), are very small for the Intercept, Age, and Education parameters. As
seen in Chapter 3, a small p-value corresponds to a small probability that such a large t value would be
observed under the assumptions of the null hypothesis. In this case, for a givenj = 0, 1, 2, … , p -1, the null
and alternate hypotheses follow:
H0 : !3i = 0 versus HA: .Bi 7:0
For small p-values, as is the case for the Intercept, Age, and Education parameters, the null
hypothesis would be rejected. For the Gender parameter, the corresponding p-value is fairly large at
0.13.1n other words, at a 90% confidence level, the null hypothesis would not be rejected. So, dropping the
variable Gender from the linear regression model should be considered. The following R code provides
the modified model results:
results2 <- lm(Income - Age + Education, income_input) summary(results2) Call: lm(formula Income - Age + Education, data income_input) Residuals: Min lQ Median 3Q Max -36.889 -7.892 0.185 8.200 37.740 Coefficients: Estimate Std. Error t value Pr(>jtjl

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
(Intercept) 6.75822 1. 92728 3.507 0.000467 ***
Age 0. 99603 0.02057 48.412 < 2e-16 *** Education 1.75860 0.11586 15.179 < 2e-16 *** Signif. codes: 0 '***' 0.001 '**I 0.01 I* I 0.05 I I 0.1 Residual standard error: 12.08 on 1497 degrees of freedom Multiple R-squared: 0.6359, Adjusted R-squared: 0.6354 F-statistic: 1307 on 2 and 1497 DF, p-value: < 2.2e-16 I I 1 Dropping the Gender variable from the model resulted in a minimal change to the estimates of the remaining parameters and their statistical significances. The last part of the displayed results provides some summary statistics and tests on the linear regression model. The residual standard error is the standard deviation of the observed residuals. This value, along with the associated degrees of freedom, can be used to examine the variance of the assumed normally distributed error terms. R-squared (R2) is a commonly reported metric that measures the variation in the data that is explained by the regression model. Possible values of R2 vary from 0 to 1, with values closer to 1 indicating that the model is better at explaining the data than values closer to 0. An R2 of exactly 1 indicates that the model explains perfectly the observed data (all the residuals are equal to O).ln general, the R2 value can be increased by adding more variables to the model. However, just adding more variables to explain a given dataset but not to improve the explanatory nature of the model is known as overfitting. To address the possibility of overfitting the data, the adjusted R2 accounts for the number of parameters included in the linear regression model. The F-statistic provides a method for testing the entire regression model. In the previous t-tests, indi- vidual tests were conducted to determine the statistical significance of each parameter. The provided F-statistic and corresponding p-value enable the analyst to test the following hypotheses: H0 :{31 =/32 = ... =f3P_1 =0 versus HA :{3i ::cO foratleastone j =1, 2, ... , p-1 In this example, the p-value of 2.2e- 16 is small, which indicates that the null hypothesis should be rejected. Categorical Variables In the previous example, the variable Gender was a simple binary variable that indicated whether a person is female or male. In general, these variables are known as categorical variables. To illustrate how to use categorical variables properly, suppose it was decided in the earlier Income example to include an additional variable, State, to represent the U.S. state where the person resides. Similar to the use of the Gender variable, one possible, but incorrect, approach would be to include a State variable that would take a value of 0 for Alabama, 1 for Alaska, 2 for Arizona, and so on. The problem with this approach is that such a numeric assignment based on an alphabetical ordering of the states does not provide a meaningful measure of the difference in the states. For example, is it useful or proper to consider Arizona to be one unit greater than Alaska and two units greater that Alabama? 6.1 Linear Regression In regression, a proper way to implement a categorical variable that can take on m different values is to add m-1 binary variables to the regression model. To illustrate with the Income example, a binary vari- able for each of 49 states, excluding Wyoming (arbitrarily chosen as the last of 50 states in an alphabetically sorted list), could be added to the model. results3 <- lm(Income-Age + Education, + Alabama, + Alaska, + Arizona, + WestVirginia, + Wisconsin, income_ input) The input file would have 49 columns added for these variables representing each of the first 49 states. If a person was from Alabama, the Alabama variable would be equal to 1, and the other 48 vari- ables would be set to 0. This process would be applied for the other state variables. So, a person from Wyoming, the one state not explicitly stated in the model, would be identified by setting all 49 state variables equal to 0. In this representation, Wyoming would be considered the reference case, and the regression coefficients of the other state variables would represent the difference in income between Wyoming and a particular state. Confidence Intervals on the Parameters Once an acceptable linear regression model is developed, it is often helpful to use it to draw some infer- ences about the model and the population from which the observations were drawn. Earlier, we saw that t-tests could be used to perform hypothesis tests on the individual model parameters, {3i, j = 0, 1, ... , p- 1. Alternatively, these t-tests could be expressed in terms of confidence intervals on the parameters. R simpli- fies the computation of confidence intervals on the parameters with the use of the conf int () function. From the Income example, the following R command provides 95% confidence intervals on the intercept and the coefficients for the two variables, Age and Education. confint(results2, level = .95) 2.5 % 97.5 % (Intercept) 2.9777598 10.538690 Age 0.955677: 1.036392 Education 1.5313393 1.985862 Based on the data, the earlier estimated value of the Education coefficient was 1.76. Using conf int (),the corresponding 95% confidence interval is (1.53, 1.99), which provides the amount of uncertainty in the estimate. In other words, in repeated random sampling, the computed confidence interval straddles the true but unknown coefficient 95% ofthe time. As expected from the earlier t-test results, none ofthese confidence intervals straddles zero. ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION Confidence Interval on the Expected Outcome In addition to obtaining confidence intervals on the model parameters, it is often desirable to obtain a confidence interval on the expected outcome. In the Income example, the fitted linear regression provides the expected income for a given Age and Education. However, that particular point estimate does not provide information on the amount of uncertainty in that estimate. Using the predict {) function in R, a confidence interval on the expected outcome can be obtained for a given set of input variable values. In this illustration, a data frame is built containing a specific age and education value. Using this set of input variable values, the predict {) function provides a 95% confidence interval on the expected Income for a 41-year-old person with 12 years of education. Age <- 41 Education <- 12 new_pt <- data.frame(Age, Education) conf_int_pt <- predict(results2,new_pt,leve1=.95,interval= 11 Confidence 11 ) conf_int_pt fit lwr upr 1 68.69884 67.83102 69.56667 For this set of input values, the expected income is $68,699 with a 95% confidence interval of ($67,831, $69,567). Prediction Interval on a Particular Outcome The previous confidence interval was relatively close{+/- approximately $900} to the fitted value. However, this confidence interval should not be considered as representing the uncertainty in estimating a par- ticular person's income. The predict {) function in R also provides the ability to calculate upper and lower bounds on a particular outcome. Such bounds provide what are referred to as prediction intervals. Returning to the Income example, in R the 95% prediction interval on the Income for a 41-year-old person with 12 years of education is obtained as follows: pred_int_pt <- predict(results2,new_pt,leve1=.95,interval= 11 prediction 11 ) pred_int_pt fit upr 1 68.69884 44.98867 92.40902 Again, the expected income is $68,699. However, the 95% prediction interval is ($44,988, $92,409). If the reason for this much wider interval is not obvious, recall that in Figure 6-3, for a particular input vari- able value, the expected outcome falls on the regression line, but the individual observations are normally distributed about the expected outcome. The confidence interval applies to the expected outcome that falls on the regression line, but the prediction interval applies to an outcome that may appear anywhere within the normal distribution. Thus, in linear regression, confidence intervals are used to draw inferences on the popula- tion's expected outcome, and prediction intervals are used to draw inferences on the next possible outcome. 6.1 Linear Regression 6.1 .3 Diagnostics The use of hypothesis tests, co nfidence inte rvals, and prediction intervals is dependent on the model assumptions being true. The following discussion provides some tools and techniques that can be used to validate a fitted linear regression model. Evaluating the Linearity Assumption A major assumption in linear regression modeling is that the relationship between the input vari ables and the ou tcome variable is linear. The most fundamental way to evaluate such a rela tionship is to plot the outcome variable against each input variable. In the Income example, such scatterplots were generated in Figure 6-4. If the relationship between Age and Income is represented as illustrated in Figure 6-5, a linear model would not apply. In such a case, it is often useful to do any of the following: • Transform the outcome variable. • Transform the input variables. • Add extra input variables or terms to the regression model. Common transformations include taking square roots or the logarithm of the variables. Another option is to create a new input variable such as the age squared and add it to the linear regression model to fit a quadratic relationship between an input variable and the outcome. 0 "' 0 0 C1>
0
(X)
E
0
0 0
!: U)
0

ro
:::J
0 “0
‘ (ii
~
0
Ci’
0
“‘i
50 60 70 80 90
fitted.values
FIGURE 6-6 Residual plot indicating constant variance
100 11 0
The plot in Fig ure 6-6 indicates that regardless of income value along the fitted linea r regression model,
the resi duals are observed somewhat evenly on both sides of the reference zero line, and t he spread of the
resi duals is fairly constant from one fitted value to t he next. Such a plot would support the mean of zero
and the constant variance assumptions on the error terms.
If the residual plot appea red like any of those in Fig ures 6-7 through 6-10, then some of the earlier
discussed transfo rmations or possible input variab le addition s shou ld be considered and attempted.
Figure 6-7 illustrates the existence of a non linea r t ren d in the residuals. Figure 6-8 illustrates that the
residuals are not centered on zero. Figure 6-9 indicates a linear trend in the residuals acro ss the various
outcomes along the linear re gression model. This plot may indicate a missing variable or term from the
regression model. Figure 6-10 provides an example in which the variance of the error terms is not a constant
but increases along the fitted linear regression model.
Evaluating th e Norm ality Assumption
The residual plots are useful for confirming that th e residuals we re centered on zero and have a con-
stant varia nce. However, the normality assumption still has to be va lidated . As shown in Figure 6-11 ,
the foll owing R code provides a histog ram plot of the residuals from res u l ts2 , the ou tput from the
Income example:
h i st(results2$residuals , mai n=”” )

6.1 Linear Regression
0
1, and as y _,- , f(y) -+ 0. So, as Figure 6-14 illustrates, the va lue of the
logistic function f(y) varies from 0 to 1 as y increases.
0
(I)
z:
c)
a.
(0 )(
C1l 0
+
:::;: ‘fz )
(Intercept) 3.415201 0.163734 20.858 <2e-:.6 Age -0.156643 0.004088 -38.320 <2e-:6 l·larried 0.066432 0.068302 0.973 0.331 Cust _years 0.017857 0.030497 0.586 0.558 Churned contacts 0.382324 0.027313 13. 998 <2e-16 - Signif. codes: o 1 *** 1 0.001 1 ** 1 0.01 1 * 1 0.05 1 • 1 0.1 I I 1 As in the linear regression case, there are tests to determine if the coefficients are significantly differ- ent from zero. Such significant coefficients correspond to small values of Pr ( > I z I } , which denote the
p-value for the hypothesis test to determine if the estimated model parameter is significantly different from
zero. Rerunning this analysis without the Cust_years variable, which had the largest corresponding
p-value, yields the following:
Churn_logistic2 <- glm (Churned-Age + Married + Churned_contacts, data=churn_input, family=binomial(link="logit")) summary(Churn_logistic2) Coefficients: Estimate stc. Error z ·la l ue Pr: > i z I’,
(Intercept l 3.472062 C.132107 26.282 <2e-16 Age -).156635 C.004088 -38.318 <2e-16 Narried ).066430 C.068299 0.973 0. 331 Churned contacts ).381909 0.027302 13.988 <2e-.;.6 - Signif. codes: 0 1 *** 1 0.001 1 ** 1 0.01 1 * 1 0.05 '. 1 0.1 1 1 1 Because the p-value for the Married coefficient remains quite large, the Married variable is dropped from the model. The following R code provides the third and final model, which includes only the Age and Churned_ contacts variables: Churn_logistic3 <- glm (Churned-Age + Churned_contacts, 6.2 Logistic Regression data=churn_input, family=binomial(link= 11 logit 11 )) summary(Churn_logistic3) Call: glm(formula Churned - Age + Churned_contacts, family binomial(link = "logit"), data churn_input} Deviance Residuals: Min 1Q Median 3Q Max -2.4599 -0.5214 -0.1960 -0.0736 3.3671 Coefficients: Estimate Std. Error z value Pr ( > i z I}
(Intercept} 3. 502716 0.128430 27.27 <2e-16 Age -0.156551 0.004085 -38.32 <2e-16 *** Churned_contacts 0.381857 O.C27297 13.99 <2e-16 *** Signif. codes: 0 1 *** 1 0.001 1 ** 1 0.01 '*' 0.05 '. 1 0.1 1 1 1 (Dispersion parameter for binomial family taken to be 1} Null deviance: 8387.3 on 7999 degrees of freedom Residual deviance: 5359.2 on 7997 degrees of freedom AIC: 5365.2 Number of Fisher Scoring iterations: 6 For this final model, the entire summary output is provided. The output offers several values that can be used to evaluate the fitted model. It should be noted that the model parameter estimates correspond to the values provided in Equation 6-11 that were used to construct Table 6-1. Deviance and the Pseudo-R2 In logistic regression, deviance is defined to be -2* logL, where Lis the maximized value ofthe likelihood function that was used to obtain the parameter estimates. In the R output, two deviance values are pro- vided. The null deviance is the value where the likelihood function is based only on the intercept term (y = /3~. The residual deviance is the value where the likelihood function is based on the parameters in the specified logistic model, shown in Equation 6-12. y = /3 0 + /31 *Age + /32 *Churned_ contacts A metric analogous to R2 in linear regression can be computed as shown in Equation 6-13. d R2 1 residual dev. pseu o- = ----- null dev. null dev. - res. dev. null dev. (6-12) (6-13) The pseudo-R2 is a measure of how well the fitted model explains the data as compared to the default model of no predictor variables and only an intercept term. A pseudo-R2 value near 1 indicates a good fit over the simple null model. ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION Deviance and the Log-Likelihood Ratio Test In the pseudo-R2 calculation, the -2 multipliers simply divide out. So, it may appear that including such a multiplier does not provide a benefit. However, the multiplier in the deviance definition is based on the log-likelihood test statistic shown in Equation 6-14: T = -2 *log [ Lnull l Lair. = -2*1og(Lnull) -(-2)*1og(L011 ) (6-14) where Tis approximately Chi-squared distributed (x~) with k degrees of freedom (df) = dfnuJJ - dfalremare The previous description of the log-likelihood test statistic applies to any estimation using MLE. As can be seen in Equation 6-15, in the logistic regression case, T =null deviance- residual deviance- x~ _ 1 (6-15) where p is the number of parameters in the fitted model. So, in a hypothesis test, a large value ofT would indicate that the fitted model is significantly better than the null model that uses only the intercept term. In the churn example, the log-likelihood ratio statistic would be this: T = 8387.3- 5359.2 = 3028.1 with 2 degrees of freedom and a corresponding p-value that is essentially zero. So far, the log-likelihood ratio test discussion has focused on comparing a fitted model to the default model of using only the intercept. However, the log-likelihood ratio test can also compare one fitted model to another. For example, consider the logistic regression model when the categorical variable Married is included with Age and Churned_ contacts in the list of input variables. The partial R output for such a model is provided here: summary(Churn_logistic2} Call: glm(formula = Churned - Age + 1'-larried + Churned_conta::::ts, family = binom:.al (1 ink = "logi t") , data = churn_input) Coefficients: Estimate Std. Error z value Pr (>I z I)
(Intercept) 3.472062 0.132107 26.282 <2e-15 *** Age -0.156635 0.004088 -38.318 <2e-15 *** Married 0.066430 0.068299 0.973 0.331 Churned contacts 0.381909 0.027302 13. 988 <2e-16 *** Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' D.l' '1 (Dispersion parameter for bino~ial family taken to be 1) Null deviance: 8387.3 on 7999 degrees of freedom Residual deviance: 5358.3 on 7996 degrees of freedom 6.2 Logistic Regression The residual deviances from each model can be used to perform a hypothesis test where H0 : f3Morried = 0 against H A : f3Married ;c 0 using the base model that includes the Age and Churned_ contacts variables. The test statistic follows: T = 5359.2- 5358.3 = 0.9 with 7997- 7996 = 1 degree of freedom Using R, the corresponding p-value is calculated as follows: pchisq(.9 , 1, lower=FALSE) [1] 0.3427817 Thus, at a 66% or higher confidence level, the null hypothesis, H0 : ('JMarried = 0, would not be rejected. Thus, it seems reasonable to exclude the variable Married from the logistic regression model. In general, this log-likelihood ratio test is particularly useful for forward and backward step-wise meth- ods to add variables to or remove them from the proposed logistic regression model. Receiver Operating Characteristic (ROC) Curve logistic regression is often used as a classifier to assign class labels to a person, item, or transaction based on the predicted probability provided by the model. In the Churn example, a customer can be classified with the label called Churn if the logistic model predicts a high probability that the customer will churn. Otherwise, a Remain label is assigned to the customer. Commonly, 0.5 is used as the default probability threshold to distinguish between any two class labels. However, any threshold value can be used depending on the preference to avoid false positives (for example, to predict Churn when actu- ally the customer will Remain) or false negatives (for example, to predict Remain when the customer will actually Churn). In general, for two class labels, C and •C, where "•C" denotes "not C," some working definitions and formulas follow: o True Positive: predict C, when actually C o True Negative: predict •C, when actually •C o False Positive: predict C, when actually •C o False Negative: predict •C, when actually C F I P . . R (FPR) # of false positives a se os1t1ve ate = f # o negatives 1i P .t. R t (TPR) # of true positives rue os1 1ve: a e = ----=---- # of positives (6-16) (6-17) The plot of the True Positive Rate (TPR) against the False Positive Rate (FPR) is known as the Receiver Operating Characteristic (ROC) curve. Using the ROCR package, the following R commands generate the ROC curve for the Churn example: library (ROCR) pred = predict(Churn_logistic3, type="response") ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION predObj = prediction(p red, churn_input$Churned ) rocObj aucObj performance(predObj, measure="tpr", x . measure="fpr") performance(predObj, measure= "auc") plot(rocObj, main= paste( "Area under the curve:", round(aucObj @y.values[[l]] ,4))) The usefulness of thi s plot in Figure 6-15 is that the preferred outcome of a cla ssifier is to have a low FPR and a high TPR. So, when moving from left to right on the FPR axis, a go od mode l/ classifier ha s the TPR rapidly approach values near 1, with only a small change in FPR. The closer the ROC curve tracks along the vertical axis and approaches the upper-left ha nd of t he plot, near the point (0,1), the better the model/cla ssifier performs. Thus, a useful metric is to compute t he area under th e ROC curve (AUC ). By examining the axes, it ca n be seen that the theoretical maximu m for t he area is 1. ~ ~ Q) > = ·u;
0
a.
Q)
2
1-
(X)
0
<0 0 ~ 0 0 0 0.0 Aiea under the Curve = 0.8877 0.2 0.4 0.6 False positive ra te FIGURE 6-15 ROC curve for the churn example 0.8 1.0 To illustrate how the FPR and TPR values are dependent on the threshold value used for the classi fi er, the plot in Figure 6-1 6 was constructed using the following R code: ~extract the alpha(threshold), FPR, and TPR values from rocObj alpha<- round(as . numeric(unlist(rocObj @alpha.values)),4) fpr <- round(as . numeric(unlist(roc0bj @x.values)),4) tpr <- round(as .nume ric(unlist(rocObj@y.values)),4) # adjust margins a:1d plot TPR a nd FPR par(mar = c(5,5 , 2 , 5)) plot(alph a,tpr, xlab= "Threshold " , xlim=c(O,l), ylab ="True positive rate " , type= " l " ) par (new="True " ) plot(alpha,fpr , xlab =" " , ylab="", axes=F, xlim=c(O,l), t ype= "l " ) axis(side=4) mtext(side=4, line=3, "False positive rate " ) text(0.18 , 0.18, " FPR") text(0.58 , 0.58 , "TPR") 6 .2 Logist ic Regression ~ Q) co ci "§ Q) ~ >
:OJ
0
·u;
0 “ 0 . .,
·u;
“
ro
N u.
ci
~
0
0.8 1.0
For a threshold value ofO, every item is cl assifi ed as a positive outcome. Thu s, the TPR value is 1. However,
all the negatives are also classified as a positive, and the FPR value is also 1. As the t hreshold value increases,
more and more negative cla ss labels are assigned. Thus, the FPR and TPR values decrease. When the thresh-
old reaches 1, no positive labels are assigned, and the FPR and TPR va lues are both 0.
For the purposes of a cl assifier, a commonly used threshold value is 0.5. A positive label is assigned for any
probability of0.5 or greater. Otherwise, a negative label is assigned. As the following R code illustrates, in t he
analysis of the Churn dataset, the 0.5 threshold corresponds to a TPR va lue of 0.56 and a FPR value of 0.08.
i < - which(round(alpha,2) == . 5) p aste( "Thr e shold=", (al p ha[i ] ), "TPR=" , t p r[i ] , "FPR=", f pr li ]) '.] "ThrE'sh "d n.c." Thus, 56% of customers who will churn are properly cla ssified with the Churn label, and 8% of t he customers who wi ll remain as customers are improperly labeled as Churn. If identifying on ly 56% of the churners is not acceptable, then the threshold could be lowered. For example, suppose it was decided to classify with a Churn label any customer with a probabi lity of churning greater than 0.15. Then the fol - lowing R code indicates that the correspondi ng TPR and FPR val ues are 0.91 and 0.29, respectively. Thus, 91% of the customers who will churn are properly identified, but at a cost of misclassifying 29% of the customers who will remain. i < - which(round(alpha,2) == .15 ) paste( " Threshold=", (alpha[i]), " TPR=", t p r[i] , "FPR=", fpr[i]) ,1] "Threshold- O. l5•l3 TPR- 0.9116 F?R= .286 .. " ~2] "Threshold 0 . 1518 TPR 0. 0 12::! FPR= 0 . 28-5" [3] "Threshold= 0.1·179 TPR= 0 . q1·l5 F?R= 0 . 2942 11 [4] "Threshold- 0 . 14r.5 TPR- o . q174 FPR 0. 2°8," The ROC cu rve is usefu l fo r evaluating other classifiers and wi ll be utilized again in Chapter 7, "Adva nced Analytical Theory and Methods: Classification." ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION 3000-. : ooo ... 'E ::J 0 u 1000 0 Histogram of the Probabilities It can be useful to visualize the observed responses against the estimated probabili ties provided by the logistic regression. Figure 6-17 provides overlaying histograms for the customers who churned and for the customers who remained as customers. With a proper fitting logistic model, the customers who remained tend to have a low probability of churning. Conversely, the customers who churned have a high probability of churning again. This histogram plot helps visualize the number of items to be properly classi fi ed or mis- classified.ln the Churn example, an ideal histogram plot would have the remaining customers grouped at the left side of the plot, the customers who churned at the right side of the plot, and no overlap of these two groups. 000 0 25 050 Pro babrllry 0 75 C., L>ff”ed
R•”‘-‘”ed
1.00
FIGURE 6-17 Customer counts versus estimated churn probability
6.3 Reasons to Choose and Cautions
Linear regression is suitable when the input variables are continuous or discrete, including categorical data
types, but the outcome variable is continuous. If the outcome variable is ca tegorical, logistic regression
is a better choice.
Both mode ls assume a linear additive function of the input variables. If such an assumption does not
hold true, both regression techniques perform poorly. Furthermore, in linear regression, the assumption of
normally distri buted error terms with a constant variance is important for many of the statistica l inferences
that can be considered. If the various assumptions do not appear to hold, the appropriate transformations
need to be applied to the data.

6.4 Additional Regression Models
Although a collection of input variables may be a good predictor for the outcome variabl e, the analyst
should not infer that the input variables directly cause an outcome. For example, it may be identified that
those individuals who have regular dentist visits may have a reduced risk of heart attacks. However, simply
sending someone to the dentist almost certainly has no effect on that person’s chance of having a heart
attack. It is possible that regular dentist visits may indicate a person’s overa ll health and dietary choices,
which may have a more direct impact on a person’s health. This example illustrates the commonly known
expression, “Correlation does not imply causation.”
Use caution when applying an already fitted model to data that falls outside the dataset used to train
the model. The linear relationship in a regression model may no longer hold at va lues outside the training
dataset. For example, if income was an input variable and the va lues of income ranged from $35,000 to
$90,000, applying the model to incomes well outside those incomes could resu lt in inaccurate estimates
and predictions.
The income regression example in Section 6.1.2 mentioned the possibility of using categori cal variab les
to represent the 50 U.S. states. In a linear regression model, t he state of residence would provide a simple
addi tive term to the income mode l but no other impact on the coefficients of the other input variables,
such as Age and Education. However, if state does influence the other variables’ impact to the income
model, an alternative approach would be to build 50 separate linear regression models: one mode l for
each state. Such an approach is an example of the options and decisions that the data scientist must be
willing to consider.
If several of the input variables are highly correlated to each ot her, the condition is known as
multicollinearity. Multicollinearity can often lead to coefficient estimates that are relatively large in abso-
lute magnitude and may be of inappropriate direction (negative or positive sign). When possible, the major-
ity of these correlated variables should be removed from the model or replaced by a new variable that is
a function of the correlated variables. For example, in a medical application of regression, height and
weight may be considered important input va ri ables, but these variables tend to be correlated . In this
case, it may be useful to use the Body Mass Index (BMI), which is a function of a person’s height and weight.
BMI = weight where weight is in kilograms and height is in meters
height 2
However, in some cases it may be necessary to use the correlated variables. The next section provides
some techniques to address highly correlated variables.
6.4 Additional Regression Models
In the case of multicollinearity, it may make sense to place some re strictions on the mag nitudes of t he
estimated coefficients. Ridge regression , which applies a penalty based on the size of the coefficients, is
one technique that can be applied. In fitting a linear regression model, the objective is to find the values
of the coefficients that minimize the sum of the residuals squared. In ridge regression, a penalty term
proportional to the sum of the squares of the coefficients is added to the sum of the residuals squared.
Lasso regression is a related modeling technique in which the penalty is proportional to the sum of the
absolute values of the coefficients.

ADVANCED ANALYTICAL THEORY AND METHODS: REGRESSION
On ly bi nary outcome va riables were examined in the use of logistic regression. if the outcome va riable
ca n assume more t han two states, m ultinomial logistic regression ca n be used.
Summary
This chapter discussed the use of linear regression and log istic regression to model historical data and to
pred ict future outcomes. Usi ng R, exa mples of each regression technique were presented . Several diag-
nostics to eval uate the models and t he underlying assum ptions were covered.
Although regression analysis is relatively straightforward to perform using many existing soft ware pack-
ages, considerable ca re must be taken in performing and interpreting a regression analysis. This chapter
highlighted that in a reg ression ana lysis, the data scientist needs to do the following:
• Determine the best input variables and their relationship to the outco me variable.
• Understand the underlying assumptions and their impact on the modeling results.
• Transform the variables, as appropri ate, to achieve adherence to the model assumptions.
• Decide whether building one comprehensive model is the best choice or consider building ma ny
models on partitions of t he data.
Exercises
1. In the Income li near regression example, consider the distribution of the outcome variable Income.
Income val ues tend to be highly skewed to the righ t (distribution of va lue has a large tail to t he
righ t). Does such a non-normally distributed outcome va riable violate the general assumption of a
linea r reg ression model? Provide supporting arguments.
2. In the use of a categorica l variable with n possible va lues, explain the following:
a. Why only n – 1 binary variables are necessary
b. Why usi ng n variables would be problematic
3. In the example of usi ng Wyoming as the reference case, discu ss the effect on the estimated model
parameters, includ ing the intercept, if another state was selected as the reference case.
4. Describe how log istic regression can be used as a cl assifier.
5. Discuss how the ROC curve can be used to determine an appropriate threshold value for a classifi er.
6. If the probab ility of an event occurring is 0.4, then
a. What is t he odds ratio?
b. What is the log odds ratio?
7. If b
3
= – .5 is an estimated coefficient in a linear regression model, what is the effect on the odds ratio
for every one unit increase in the value of x
3
?

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
In addition to analytical methods such as clustering (Chapter 4, “Advanced Analytical Theory and Methods:
Clustering”), association rule learning Chapter 5, “Advanced Analytical Theory and Methods: Association
Rules”, and modeling techniques like regression (Chapter 6, “Advanced Analytical Theory and Methods:
Regression”), classification is another fundamental learning method that appears in applications related
to data mining. In classification learning, a classifier is presented with a set of examples that are already
classified and, from these examples, the classifier learns to assign unseen examples. In other words, the
primary task performed by classifiers is to assign class labels to new observations. Logistic regression
from the previous chapter is one of the popular classification methods. The set of labels for classifiers is
predetermined, unlike in clustering, which discovers the structure without a tra ining set and allows the
data scientist optiona lly to create and assign labels to the clusters.
Most classification methods are supervised, in that they start with a training set of prelabeled observa-
tions to learn how likely the attributes of these observations may contribute to the classification of future
unlabeled observations. For example, existing marketing, sales, and customer demographic data can be
used to develop a classifier to assign a “purchase” or “no purchase” label to potential future customers.
Classification is widely used for prediction purposes. For example, by building a classifier on the tran-
scripts of Un ited States Congressional floor debates, it can be determined whether the speeches represent
support or opposition to proposed legislation [1]. Classification can help health care professionals diagnose
heart disease patients [2]. Based on an e-mail’s content, e-mail providers also use classification to decide
whether the incoming e-mail messages are spam [3].
This chapter mainly focuses on two fundamental classification methods: decision trees and
nai”ve Bayes.
7.1 Decision Trees
A decision tree (also called prediction tree) uses a tree structure to specify sequences of decisions and
consequences. Given input X = {x
1
, x
1
, ••• xn) , the goal is to predict a response or output variable Y. Each
memberofthe set { x
1
,x
1
, … xn} is called an input variab le. The prediction can be achieved by constructing
a decision tree with test points and branches. At each test point, a decision is made to pick a specific branch
and traverse down the tree. Eventually, a final point is reached, and a prediction can be made. Each test
point in a decision tree involves testing a particular input variable (or attribute), and each branch represents
the decision being made. Due to its flexibility and easy visualization, decision trees are commonly deployed
in data mining applications for classification purposes.
The input values of a decision tree can be categorical or continuous. A decision tree employs a structure
of test points (ca lled nodes) and branches, which represent the decision being made. A node without fur-
ther branches is called a l eaf n ode. The leaf nodes return class labels and, in some implementations, they
return the probability scores. A decision tree can be converted into a set of decision rules. In the following
example rule, income and mortgage_ amount are input variables, and the response is the output
variable d efa ult with a probability score.
IF .1. ·om
THEN l<>f tu. t Tt•.te WITH PROBABILITY 75%

7.1 Decision Trees
Decision trees have two varieties: classification trees and regression trees. Classification trees usu-
ally apply to output variables that are categorical-often binary-in nature, such as yes or no, purchase
or not purchase, and so on. Regression trees, on the other hand, can apply to output variables that are
numeric or continuous, such as the predicted price of a consumer good or the likelihood a subscription
will be purchased.
Decision trees can be applied to a variety of situations. They can be easily represented in a visual way,
and the corresponding decision rules are quite straightforward. Additionally, because the result is a series
of logical if- then statements, there is no underlying assumption of a linear (or nonlinear) relationship
between the input variables and the response variable.
7.1.1 Overview of a Decision Tree
Figure 7-1 shows an example of using a decision tree to predict whether customers will buy a product.
The term branch refers to the outcome of a decision and is visualized as a line connecting two nodes. If a
decision is numerical, the “greater than” branch is usually placed on the right, and the “less than” branch
is placed on the left. Depending on the nature of the variable, one of the branches may need to include
an “equal to” component.
Internal nodes are the decision or test points. Each internal node refers to an input variable or an
attribute. The top internal node is called the root. The decision tree in Figure 7-1 is a binary tree in that each
internal node has no more than two branches. The branching of a node is referred to as a split.
<---------------------------- Root- Top internal node '----::311'01;::---' Male <---------------------Branch - Outcome of test <--------------Internal Node- Decision on variable FIGURE 7-1 Example of a decision tree Sometimes decision trees may have more than two branches stemming from a node. For example, if an input variable Weather is categorical and has three choices-Sunny, Rainy, and Snowy-the corresponding node Weather in the decision tree may have three branches labeled as Sunny, Rainy, and Snowy, respectively. The depth of a node is the minimum number of steps required to reach the node from the root. In Figure 7-1 for example, nodes Income and Age have a depth of one, and the four nodes on the bottom of the tree have a depth of two. Leaf nodes are at the end of the last branches on the tree. They represent class labels-the outcome of all the prior decisions. The path from the root to a leaf node contains a series of decisions made at vari- ous internal nodes. ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION In Figu re 7-1, the root nod e spl its into two branches with a Gender tes t. The right branch contains all those records with the vari able Gende r equal to Male, and the left branch contai ns all those record s with t he variable Gen der equal to Female to create the depth 1 internal nodes. Each intern al node effec- tively acts as the root of a subtree, and a best test for each node is determ ined independently of the other internal nodes. The left-hand side (LHS) internal node splits on a question based on the Income varia ble to create leaf nodes at depth 2, whereas the right-hand side (RHS) splits on a question on the Age variable. The decision tree in Figu re 7-1 shows that females with income less than or equal to $45,000 and males 40 years old or younger are classifi ed as people who would pu rchase the product. In traversi ng this tree, age does not matter for fema les, and income does not matter for males. Decision trees are widely used in practice. For example, to classify animals, questions (like cold-blooded or warm -blooded, mammal or not mammal) are answered to arrive at a certain classification. Another example is a checklist of symptoms during a doctor's evaluation of a patient. The artificial intelligence engine of a video game commonly uses decision trees to control the autonomous actions of a character in response to various scenarios. Retailers can use decision trees to segment customers or predict response rates to marketing and promotions. Financial institutions can use decision trees to help decide if a loan app lica tion should be approved or denied. In the case of loan approval, com puters can use the logical if- then statements to pred ict whether the customer will default on the loan. For customers with a clear (strong) outcome, no human interaction is req uired; for observations that may not generate a clear response, a human is needed for the decision. By limiting the number of splits, a short tree can be created. Short trees are often used as component s (also cal led weak l earners or base learn ers) in ensemble m etho ds. Ensemble methods use multiple predictive models to vote, and decisions ca n be made based on the combination of the votes. Some popu- lar ensemble methods include random forest [4]. bagging, and boosting [5). Section 7.4 discusses these ensemble methods more. Th e sim plest short tree is called a decision stu mp, which is a decision tree with the root immediately connected to the leaf nodes. A decision stump makes a pred iction based on the value of j ust a single input variable. Figure 7-2 shows a decision stump to classify two species of an iris flower based on the petal width. The figure shows that, if the petal width is smaller than 1.75 centimeters, it's Iris versicolor; otherwise, it's Iris virgin ica. FIGURE 7-2 Example of a decision stump To illustrate how a decision tree work s, consider th e case of a bank t hat wa nts to market its term deposit products (such as Certificates of Deposit) to the appropriate customers. Given the demographics of clients and their reactions to previous campaign phone calls, the bank's goal is to predict which clients would subscribe to a term deposit. The dataset used here is based on the original dataset collected from a Portuguese bank on directed marketing campaigns as stated in the work by Moro et al. [6). Figure 7-3 shows a subset of the modified bank marketing dataset. This dataset includes 2,000 insta nces randomly drawn 7.1 Decision Trees from the original dataset, and each instance corresponds to a customer. To make the example simple, the subset only keeps the following ca tegori cal variables: (1) job, (2) marita l statu s, (3) education level, (4) if the credit is in default, (5) if there is a housing loan, (6) if the customer currently has a personal loan, (7) con tact type, (8) result of the previous marke ting campaign contact (pou tcome), and finally (9) if the client actually subscribed to the term deposit. Attributes (1) through (8) are input variables, and (9) is considered the outcome. The ou tcome subscribed is either yes (meaning the customer will subscribe to the term deposit) or no (meaning the customer won't subscribe). All the vari ables listed earlier are categori cal. job ml(iUI Nuudoo dcfwlt houWng lo.on contKI poultomc wbw:r~ ••naac~~ent s inalc ter-tiary ,., ccllulal" unlcnown entrepreneur aarricd tcrtilry no , .. ,., cellular' unlcnown services diYOt"CCCI SCCOtW:IIr')' no cellular unlo:now-n ,., CIIRIJt iHnt Nt"rieil u rtl a ry , .. c e llular unknown ••n•acNnt urrico secondary no , .. unlie.no.:n unknown UMIJC IICRt sinJlC tcr tlar")' no ,., unknown unl(no..-n e ntrepreneu r a ar ricd t cr th ry ,., cdluler hUurc ,., adal n. • a,.ricd secondary cellulel'" unknown blue-collar a ardcd secondary ,., cellula r- o the r 10 aiRIJCIU!R t • • r ricd tt: r thry ,., cellular unknown II blue• co llar • er ricd s e conda ry no ,., ce llula r unlc.nown 12 ••n•u~nt ohorcco s e conda ry unknown unlenOWin I) blue-co l lar NrriC'Cl SCCOt\011")' no , .. cclluhl'" unl(no-n no 14 ret i red urdco seconda ry cellular unl(nown 15 unacetttnt sinclc tcrthry ,., cclluler unknown 16 rctlrlt(l urd~ secondary , .. ,., cellular urltnown 17 unc•ploy~ urd~ seconda ry no ,., telephone urltnown 18 unaceacnt divorced ttrt:iary , .. cclluhr urlc nown 19 ••n•cercnt aerdcd tertiary ,., cellular urlm nt : 1~3 mat·r 1ed 12 l seconda :.-y: , y
tt.ChillCl 1t :33 single : 57l tertl HT
.ttlmin. : •. 3 c •Jr.kno·,·:n

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
housing
no : 916
yes:1084
loan
no :1717
yes: 283
contact
cellular :1287
telephone: 136
unknown : 577
may
jul
aug
jun
nov
ap~·
month pout come
:581 failure: 210
; 34 0 other 79
:278 success: 58
:~32 unknmm: 1653
:183
:ES
1 Other’i :268
subsc:·ibed
no :1789
yes: 211
Attribute job includes the following values.
admin. blue-collar entrep~·eneur
235 435 70
management retired self -emplo;.·ed
423 92 69
student technician unemployed
36 339 60
houser.1aid
63
16 8
unknm·:n
10
Figure 7-4 shows a decision tree built over the bank marketing dataset. The root of the tree shows that
the overall fraction of the clients who have not subscribed to the term deposit is 1,789 out of the total
population of 2,000.
poutcome = fallure,other,unknown
poutcome =success
education = secondary,tertiary
~
~
education = primary,unknown
job = admln.,blue-collar,management,retired,services,technician
~
~
job = self-employed,student,unemployed
~
l§J
FIGURE 7-4 Using a decision tree to predict if a client will subscribe to a term deposit
~
l§)

7.1 Decision Trees
At each split, the decision tree algorithm picks the most informative attribute out of the remaining
attributes. The extent to which an attribute is informative is determined by measures such as entropy and
information gain, as detailed in Section 7.1.2.
At the first split, the decision tree algorithm chooses the pout come attribute. There are two nodes at
depth=l. The left node is a leaf node representing a group for which the outcome of the previous market-
ing campaign contact is a f ai 1 ure, other, or unknown. For this group, 1,763 out of 1,942 clients have
not subscribed to the term deposit.
The right node represents the rest of the population, for which the outcome of the previous marketing
campaign contact is a success. For the population of this node, 32 out of 58 clients have subscribed to
the term deposit.
This node further splits into two nodes based on the education level. If the education level is either
secondary or tertiary, then 26 out of 50 ofthe clients have not subscribed to the term deposit. If
the education level is primary or unknown, then 8 out of 8 times the clients have subscribed.
The left node at depth 2 further splits based on attribute job. If the occupation is admin,
blue collar, management, retired, services, or technician, then 26 out of 45 clients
have not subscribed.lfthe occupation is self -employed, student, or unemployed, then 5 out
of 5 times the clients have subscribed.
7.1.2 The General Algorithm
In general, the objective of a decision tree algorithm is to construct a tree T from a training set s.lf all the
records ins belong to some class c(subscribed =yes, for example), or if sis sufficiently pure (greater than
a preset threshold), then that node is considered a leaf node and assigned the label c. The purity of a node
is defined as its probability of the corresponding class. For example, in Figure 7-4, the root
P( subscribed= yes)= 1- 1789 = 1 0.55%; therefore, the root is only 10.55% pure on the subscribed= yes
2000
class. Conversely, it is 89.45% pure on the subscribed= no class.
In contrast, if not all the records in s belong to class cor if sis not sufficiently pure, the algorithm
selects the next most informative attribute A (duration, marital, and so on) and partitions s according
to A’s values. The algorithm constructs subtrees r,, T2 ••• for the subsets of s recursively until one of the
following criteria is met:
o All the leaf nodes in the tree satisfy the minimum purity threshold.
o The tree cannot be further split with the preset minimum purity threshold.
o Any other stopping criterion is satisfied (such as the maximum depth of the tree).
The first step in constructing a decision tree is to choose the most informative attribute. A common way
to identify the most informative attribute is to use entropy-based methods, which are used by decision tree
learning algorithms such as 103 (or Iterative Dichotomiser 3) [7] and C4.5 [8]. The entropy methods select
the most informative attribute based on two basic measures:
o Entropy, which measures the impurity of an attribute
o Information gain, which measures the purity of an attribute

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
Given a class X and its label x E X , let P(x) be the probability of x. H , ,the entropy of X, is defined as
shown in Equation 7-1.
Hx =-l.”: P(x)log
1
P(x)
(7-1}
·:• X
Equation 7-1 shows that entropy Hx becomes 0 when all P(x ) is 0 or 1. For a binary classification (true
or false}, Hx is zero if P(x) the probability of each label x is either zero or one. On the ot her hand, Hx
achieves the maximum entropy when all the class labels are equally probable. For a binary classification,
H x = 1 if the probability of all cl ass labels is SO/SO. The maximum entropy increases as the number of pos-
sible outcomes increases.
As an example of a binary random va riable, consider tossing a coin with known, not necessarily
fair, probabilities of coming up heads or tails. The corresponding entropy graph is shown in Figure 7-5. Let
x = 1 represent heads and x = 0 represent tails. The entropy of the unknown result of the next toss is maxi-
mized when the coin is fair. That is, when heads and tails have equal probability P( x = 1) = P( x = o) = 0.5,
entropy H x = – (0.5 x log
1
0.5 + 0.5 x log
2
0.5) = 1 . On the other hand, if the coin is not fair, the probabilities
of heads and tails would not be equal and there would be less uncertainty. As an extreme case, when the
probability of to ssing a head is equal to 0 or 1, the entropy is minimized to 0. Therefore, the entropy for a
completely pure variable is 0 and is 1 for a set with equal occurrences for both the classes (head and tail, or
yes and no}.
~
~
(I)
0
ro · As seen previously,
P(subscribed = yes) = 0.1055 and P(subscribed = no) = 0.8945. According to Equation 7-1, the base entropy
H,ub•wt>ro = – 0.1055·1og
2
0.1055 0.8945 ·log2 0.8945 :::::: 0.4862.

7.1 Decision Trees
The next step is to identify the conditional entropy for each attribute. Given an attribute x, its value x,
its outcome Y, and its value y, conditional entropy HYJx is the remaining entropy ofY given X, formally defined
as shown in Equation 7-2.
HYIX = .Z:::.:P( x )H(YIX = x)
=- .2::.’.: P(x) .2::.’.: P(ylx)log
2
P(ylxl
(7-2)
‘?xr x “‘yCY
Consider t he banki ng marketing scenario, if the attribu te contact is chosen, X = {c e 1 1 ul ar,
telepho ne, unknown}. The conditional entropy of conta ct considers all three values.
Table 7·1 lists the probabilities related to the contact attribute. The top row ofthe table displays the
probabilities of each value of the attribute. The next two rows contain the probabilities of the class labels
conditioned on the contact.
TABLE 7-1 Conditional Entropy Example
Cellular Telephone Unknown
P(contact) 0.6435 0.0680 0.2885
P(subscribed=yes I contact) 0.1399 0.0809 0.0347
P(subscribed=no I contact) 0.8601 0.9192 0.9653
The conditional entropy of the contact attribute is computed as shown here.
H,ubsc•.~omoct = – ( 0.6435· ( 0.1399·1og2 0.1399 + 0.8601 ·1og2 0.8601)
+ 0.0680 · ( 0.0809 ·log2 0.0809 + 0.9192 ·log2 0.9192)
+ 0.2885 · ( 0.0347 ·log
2
0.0347 + 0.9653 ·log
2
0.9653 )]
= 0.4661
Computation inside the parentheses is on the entropy of the class labels within a single contact
value. Note that the condi tiona l entropy is always less than or equa l to th e base entropy- th at is,
H ,ubswbtdjmawat ~ H ,ubswt>ed· The conditional entropy is sma ll er than the base entropy when the attribute
and the outcome are correlated. In the worst case, when the attribute is uncorrelated with the outcome,
the cond itional entropy equals t he base entropy.
The information gain of an attribute A is defined as the difference between the base entropy and the
conditional entropy of the attribute, as shown in Equation 7-3.
(7·3)
In th e bank marketing example, the information gain of the contact attribute is shown in
Equation 7-4.
= 0.4862-0.4661 = 0.0201 (7-4)

ADVANCED ANA LYT ICAL THEORY AN D METHO DS: CLASSIFICATION
Information gain compares the degree of purity of the parent node before a split with the degree of
purity of the child node after a split. At each split, an attribute with the greatest information gain is consid-
ered the most informative attribute. Information gain indicates the purity of an attribute.
The result of information gain for all the input variables is shown in Table 7-2. Attribute p o u tcome has
the most information gain and is the most informative variable. Therefore, pout come is chosen for the
first split of the decision tree, as shown in Figure 7-4. The values of information gain in Table 7-2 are small
in magnitude, but the relative difference matters. The algorithm splits on the attribute with the largest
information gain at each round.
TABLE 7-2 Calculating Information Gain of Input Variables for the First Split
Attribute ! Information Gain
pout come 0.0289
c ontact 0.0201
ho us ing 0.0133
job 0.0101
e du c ation 0.0034
mar ital 0.0018
l oan 0.0010
de fault 0.0005
Detecting Significant Splits
Quite often it is necessary to measure the significance of a split in a decision tree, especially when
the information gain is small, like in Table 7-2.
Let NA and N8 be the number of class A and class 8 in the parent node. Let NAt represent the number
of class A going to the left child node. N81 represent the number of class 8 going to the left child node,
NAR represent the number of class 8 going to the right child node, and NBR represent the number of
class 8 going to the right child node.
Let p
1
and pR denote the proportion of data going to the left and right node, respectively.
P
– NAt + NBL
L –
NA + NB

7.1 Decision Trees
The following measure computes the significance of a split. In other words, it measures how much
the split deviates from what would be expected in the random data.
where
If K is small, the information gain from the split is not significant. If K is big, it would suggest the
information gain from the split is significant.
Take the first split of the decision tree in Figure 7-4 on variable pout come for example.
NA = 1789,NB =2ll,NAL = l763,NBL = 179,NAR = 26,NBR = 32.
Following are the proportions of data going to the left and right node.
PL = 194_%’ooo = 0.971 and PR = 5%ooo = 0.029.
The N~L’ N~L’ N~R’ and N~R represent the number of each class going to the left or right node if the
data is random. Their values follow.
N~L = 1737.119,N~L = 204.881,N~R = 51.881 and N~R = 6.119
Therefore, K = 126.0324, which suggests the split on pout come is significant.
After each split, the algorithm looks at all the records at a leaf node, and the information gain of each
candidate attribute is calculated again over these records. The next split is on the attribute with the high-
est information gain. A record can only belong to one leaf node after all the splits, but depending on the
implementation, an attribute may appear in more than one split of the tree. This process of partitioning the
records and finding the most informative attribute is repeated until the nodes are pure enough, or there
is insufficient information gain by splitting on more attributes. Alternatively, one can stop the growth of
the tree when all the nodes at a leaf node belong to a certain class (for example, subscribed= yes} or
all the records have identical attribute values.
In the previous bank marketing example, to keep it simple, the dataset only includes categorical vari-
ables. Assume the dataset now includes a continuous variable called duration -representing the number
of seconds the last phone conversation with the bank lasted as part of the previous marketing campaign.
A continuous variable needs to be divided into a disjoint set of regions with the highest information gain.

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
A brute-force method is to consider every value of the continuous variable in the training data as a candi-
date split position. This brute-force method is computationally inefficient. To redu ce the complexity, the
training records can be sorted based on the duration, and the candidate splits can be identified by taking
the midpoints between two adjacent sorted values. An examples is if the duration consists of sorted values
{140, 160, 180, 200} and the candidate splits are 150, 170, and 190.
Figure 7-6 shows what the decision tree may look like when considering the durat ion attribute. The
root splits into two partitions: those clients with duration < 456 seconds, and those with duration ;:: 456 seconds. Note that for aesthetic purposes, labels for the job and contact attributes in the figure are abbreviated. dur~bon < 456 poutcome • ftr ,oth,unk poutcome = "ec Q dur<1l1on < 150 \ G6 duration >= 150
no
1785 I 2000
dur~tion >”‘ 456
conuct • ttp,unk
g
dur-ation < 1004 \ G6 durabon ;.• 1004 ~ ~ conU~ct = ell duration < 700 0 ·-~ job • <1d ,h sm ,rtt,uNn . job "" -.
4
} -of two attributes
(x
1
and x). The corresponding decision tree is on the right side of the figure. A decision surface corresponds
to a leaf node of the tree, and it can be reached by traversing from the root of the tree following by a seri es
of decisions according to the value of an attribute. The decision surface can only be axis-aligned for the
decision tree.
E
~ · B
c D
A
A B c D E
FIGURE 7-8 Decision surfaces can only be axis-aligned
The structure of a decision tree is sensitive to small variations in the training data. Although the dataset is
the same, constructing two decision trees based on two different subsets may result in very different trees. If
a tree is too deep, overfitting may occur, because each split reduces the training data for subsequent splits.
Decision trees are not a good choice if the dataset contains many irrelevant variables. This is different
from the notion that they are robust with redundant variables and correlated variables. If the dataset

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
contains redundant variables, the resulting decision tree ignores all but one of these variables because
the algorithm cannot detect information gain by including more redundant variables. On the other hand,
if the dataset contains irrelevant variables and if these variables are accidentally chosen as splits in the
tree, the tree may grow too large and may end up with less data at every split, where overfitting is likely
to occur. To address this problem, feature selection can be introduced in the data preprocessing phase to
eliminate the irrelevant variables.
Although decision trees are able to handle correlated variables, decision trees are not well suited when
most of the variables in the training set are correlated, since overfitting is likely to occur. To overcome
the issue of instability and potential overfitting of deep trees, one can combine the decisions of several
randomized shallow decision trees-the basic idea of another classifier called random forest [4]-or use
ensemble methods to combine several weak learners for better classification. These methods have been
shown to improve predictive power compared to a single decision tree.
For binary decisions, a decision tree works better if the training dataset consists of records with an even
probability of each result. In other words, the root of the tree has a 50% chance of either classification.
This occurs by randomly selecting training records from each possible classification in equal numbers. It
counteracts the likelihood that a tree will stump out early by passing purity tests because of bias in the
training data.
When using methods such as logistic regression on a dataset with many variables, decision trees can help
determine which variables are the most useful to select based on information gain. Then these variables
can be selected for the logistic regression. Decision trees can also be used to prune redundant variables.
7.1.5 Decision Trees in R
In R, rpart is for modeling decision trees, and an optional package rpart . plot enables the plotting
of a tree. The rest of this section shows an example of how to use decision trees in R with rpart . p 1 ot
to predict whether to play golf given factors such as weather outlook, temperature, humidity, and wind.
In R, first set the working directory and initialize the packages.
setwd { “c: /”)
install.packages(“rpart.plot”) # install package rpart.plot
library(“rpart”) # load libraries
library(“rpart.plot”)
The working directory contains a comma-separated-value (CSV) file named DTdata. csv. The file
has a header row, followed by 10 rows of training data.
Play,Outlook,Temperature,Humidity,Wind
yes,ra:ny,cool,normal,FALSE
no,rainy,cool,normal,TRUE
yes,overcast,hot,high,FALSE
no,sunny,mild,high,FALSE
yes,ra~ny,cool,normal,FALSE
yes,sunny,cool,normal,FALSE
yes,rainy,cool,normal,FALSE
yes,sunny,hot,normal,FALSE
yes,overcast,mild,high,TRUE
no,sunny,mild,high,TRUE

7.1 Decision Trees
The CSV file contains five attributes: Play, Outlook, Temperature, Humidity, and
Wind. Playwould be the output variable (or the predicted class}, and outlook, Temperature,
Humidity, and Wind would be the input variables. In R, read the data from the CSV file in the working
directory and display the content.
play_decision <- read. table ( 11 DTdata. csv", header=TRUE, sep=", ") play_decision Play Outlook Ternperatur·e Humidity ;·iind 1 yes rainy cool n::Jrmal FALSE 2 no r-a1n:/ cool rDrmal TRUE yes overcast hot high FJ:..LSE 4 no sun~y r.li ld high F.Z..LSE 5 yes rain;.· cool r:::Jrmal FALSE 6 yes sunny cool :nnnal FJ:..LSE 7 )'es rai::y cool r,:Jrmal FJ:,.LSE yes sunny hot rDrmal FJ..LSE 9 yes overcast mild high TRUE 10 no sunny mild high TRUE Display a summary of play_decision. summary(play_decision) Play· no :3 yes:7 Outlook Temperature Humidity overcast::?. rain;.· sunny :4 cool:5 bot :2 mild:3 high :·l normal:6 ·::ind :•loje :logical FJ.<.i~SE: 7 TRUE :3 NA's :0 The rpart function builds a model of recursive partitioning and regression trees [9]. The following code snippet shows how to use the rpart function to construct a decision tree. fit <- rpart(Play- Outlook+ Temperature +Humidity+ Wind, method="class 11 , data=play_decision, control=rpart.control(minsplit=l), parms=list(split=•information•)) The rpart function has four parameters. The first parameter, Play - Out look + Temperature + Hurnidi ty + Wind, is the model indicating that attribute Play can be predicted based on attri- butes Outlook, Temperature, Humidity, and Wind. The second parameter, method, is set to "class," telling R it is building a classification tree. The third parameter, data, specifies the dataframe containing those attributes mentioned in the formula. The fourth parameter, control, is optional and controls the tree growth. In the preceding example, control=rpart. control (minspli t=l) requires that each node have at least one observation before attempting a split. The rninspl it= 1 makes sense for the small dataset, but for larger datasets rninspl it could be set to 10% of the dataset size to combat overfitting. Besides minsp 1 it, other parameters are available to control the construction of the decision tree. For example, rpart. control (rnaxdepth=10, cp=O. 001) limits the depth ofthe ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION tree to no more than 10, and a split must decrease the overall lack of fit by a factor of 0.001 before being attempted. The last parameter (parms) specifies the purity measure being used for the splits. The value of sp 1 it can be either information (for using the information gain) or g ini (for using the Gini index). Enter sununary (fit) to produce a summary of the model built from rpart. The output includes a summary of every node in the constructed decision tree. If a node is a leaf, the out- put includes both the predicted class label (yes or no for Play) and the class probabilities-P (Play). The leaf nodes include node numbers 4, 5, 6, and 7.1f a node is internal, the output in addition displays the number of observations that lead to each child node and the improvement that each attribute may bring for the next split. These internal nodes include numbers 1, 2, and 3. summary(fit) Call: rpart(formula = P:ay- Outlook t Temperature+ Humidity+ Wind, data= play_dec:..sion, method= "class", parrr.s = list 1:sp'Cit = "in!'ormation"), control= rpart.controllminsplit 1) i n= 10 CP nsplit rel error xerror xstd 0.3333333 2 0.0100000 1.000000 0.~830~59 1.666667 0.5270~63 Variable importance Wind Outlook Temperature 51 29 20 Node number 1: 10 obse!·vations, complexity param=0.3333333 predicted c:ass=yes expected loss=0.3 P 1 nodel class counts: 7 probabilities: 0.300 J.700 left son=2 I 3 obs :· rig:1t son=3 ! 7 obs • Primary splits: Temperature splits as RRL, :..rr.prove=1. 3282860, 10 missing) \'lind < 0.5 to the !·ight, in~prove=1.3282860, ·.0 missing) Outlook splits as ?.LL, Humidity splits as LE, Surrogate splits: improve=0.8161371, (0 missing) improve=C.6326870, (0 missing; Wind< 0.5 to the right, agree=O.B, adj=0.333, 0 split' Node number 2: 3 observations, complexity param=0.3333333 predicted class=no expected loss=0.3333333 PinojeJ =0.3 class counts: probabilities: 0.667 0.333 left son=4 (2 obs) right son=S (1 obsJ Primar}· splits: Outlook splits as R-L, improve=l.9095430, (0 missing) \·lind < O.S :-.o the left, improve=0.523248l, (0 missing) Node number 3: 7 observat io:1s, complexi t·_.: pa 0.6
I 0~ ! <~ ® ~ @ ~ FIGURE 7-9 A decision tree built from DTdata.csv The decisions in Figure 7-9 are abbreviated. Use the following command to spell out the full names and display the classification rate at each node. rpart.plot(fitl type=4 1 extra=2~ clip.right.labs=FALSE, varlen~0 1 faclen=O) The decision tree can be used to predict outcomes for new datasets. Consider a testing set that contains the following record. Outlook= 11 rainy 11 , Temperature="mild 11 , Humidity= 11 high 11 , Wind=FALSE The goal is to predict the play decision of this record. The following code loads the data into Rasa data frame newda ta. Note that the training set does not contain this case. newdata <- data.frame(Outlook= 11 rainy 11 , Temperature= 11 mild 11 , Humidity= 11 high", Wind::FALSE) newdata Outlook Temperature Humidity Wind 1 rainy mild high FALSE Next, use the predict function to generate predictions from a fitted rpart object. The format of the predict function follows. predict(object 1 newdata = list(), type :: C ("vector II 1 11 probll 1 11 Classll 1 11 matrix")) 7.2 Na..-ve Bayes Parameter type is a cha racter string denoting the type of the predicted value. Set it to either p rob or clas s to predict using a decision tree model and receive the result as either the class probabi lities or just the class. The output shows that one instance is classified as Play=no, and zero instances are classified as P 1 a y= y e s . Therefore, in both cases, the decision tree predicts that the play decision of the testing set is not to play. predi ct (fit,newdata =ne wdat a, type= "prob " ) no ye-s c p redict (fi t ,newdata=newdata,type="clas s ") no Levels : r.o yes 7.2 Na·ive Bayes Na'ive Bayes is a probabilistic classification method based on Bayes' theorem (or Bayes' law) with a few tweaks. Bayes' theorem gives the relationship between the probabilities of two events and their conditional probabilities. Bayes' law is named after the English mathematician Thomas Bayes. A na'ive Bayes classifier assumes that the presence or absence of a particular feature of a class is unre- lated to the presence or absence of other features. For example, an object ca n be classified based on its attribu tes such as shape, color, and we ight. A reasonable classification for an object that is spherical, yellow, and less than 60 grams in weight may be a tennis ball. Even if these features depend on each other or upon the existence of the other featu res, a na'ive Bayes classifier considers all these properties to contribute independently to the probabil ity that the object is a tennis ball. The input variables are generally categorical, but variations of the algorithm can accept continuous variables. There are also ways to convert continuous variables into ca tegorical ones. This process is often referred to as the discre tization of continuous variables. In the ten nis ball example, a continuous variable such as weight can be grouped into intervals to be converted into a categorical variable. For an attribute such as income, t he attribute can be converted into categorical values as shown below. • Low Incom e: income < $10,000 • Working Class: $10,000 s income < $50,000 • Middle Class: $50,000 s income < $1,000,000 • Upper Class: income ~ $1,000,000 The output typica lly includes a class label and its correspo nding probability score. The probability score is not t he true probabi lity of the class label, but it's proportional to the true probability. As shown later in the chapter, in most implementations, the output includes the log probabili ty for the class, and class labels are assigned based on the highest values. ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION Because na"ive Bayes classifiers are easy to implement and can execute efficiently even without prior knowledge of the data, they are among the most popular algorithms for classifying text documents. Spam filtering is a classic use case of na'ive Bayes text classification. Bayesian spam filtering has become a popular mechanism to distinguish spam e-mail from legitimate e-mail. Many modern mail clients implement vari- ants of Bayesian spam filtering. Na"ive Bayes classifiers can also be used for fraud detection [11]. In the domain of auto insurance, for example, based on a training set with attributes such as driver's rating, vehicle age, vehicle price, historical claims by the policy holder, police report status, and claim genuineness, na"ive Bayes can provide probability- based classification of whether a new claim is genuine [12]. 7.2.1 Bayes' Theorem The conditional probability of event C occurring, given that event A has already occurred, is denoted as P(CjA) , which can be found using the formula in Equation 7-6. P(CjA)= P(AnC) P(A) (7-6) Equation 7-7 can be obtained with some minor algebra and substitution of the conditional probability: P(CjA)= P(A!C)·P(C) P(A) (7-7) whereCistheclasslabel C E {c1,c2 , . .. c) and A is the observed attributes A= {a1,a2 , ... am} . Equation 7-7 is the most common form of the Bayes' theorem. Mathematically, Bayes' theorem gives the relationship between the probabilities of C and A, P(C) and P(A), and the conditional probabilities of C given A and A given C, namely P(CjA) and P(AIC). Bayes' theorem is significant because quite often P(C I A) is much more difficult to compute than P(A I C) and P(C) from the training data. By using Bayes' theorem, this problem can be circumvented. An example better illustrates the use of Bayes' theorem. John flies frequently and likes to upgrade his seat to first class. He has determined that if he checks in for his flight at least two hours early, the probability that he will get an upgrade is 0.75; otherwise, the probability that he will get an upgrade is 0.35. With his busy schedule, he checks in at least two hours before his flight only 40% of the time. Suppose John did not receive an upgrade on his most recent attempt. What is the probability that he did not arrive two hours early? let C= {John arrived at least two hours early}, and A= {John received an upgrade}, then -,c ={John did not arrive two hours early}, and -,A= {John did not receive an upgrade}. John checked in at least two hours early only 40% of the time, or P(C)=0.4. Therefore, P( -,() =1-P(C)=0.6. The probability that John received an upgrade given that he checked in early is 0.75, or P( A I C)= 0.75. The probability that John received an upgrade given that he did not arrive two hours early is 0.35, or P(AI-,C) = 0.35. Therefore, P(•AI•C) = 0.65. The probability that John received an upgradeP(A) can be computed as shown in Equation 7-8. P(A)= P(AnC) +P(An•C) = P( C)·P(A IC)+P( ·C)·P(AI·C) = 0.4 X 0.75 + 0.6 X 0.35 =0.51 7.2 Na'ive Bayes (7-8) Thus, the probability that John did not receive an upgrade P( •A) = 0.49. Using Bayes' theorem, the probability that John did not arrive two hours early given that he did not receive his upgrade is shown in Equation 7-9. P( .q ·A) = P( ·AI•C) · P( •C) P( ·A) 0.65 X 0.6 ::::;: 0.7 96 0.49 (7-9) Another example involves computing the probability that a patient carries a disease based on the result of a lab test. Assume that a patient named Mary took a lab test for a certain disease and the result came back positive. The test returns a positive result in 95% of the cases in which the disease is actually present, and it returns a positive result in 6% of the cases in which the disease is not present. Furthermore, 1% of the entire population has this disease. What is the probability that Mary actually has the disease, given that the test is positive? let c = {having the disease} and A = {testing positive}. The goal is to solve the probability of having the disease, given that Mary has a positive test result, P(CjA). From the problem description, P( C)= 0.01, P( ·C)= 0.99, P(AIC) = 0.95 and P(AI·C) = 0.06. Bayes' theorem defines P(C I A)= P(A I C)P(C) I P(A). The probability of testing positive, that is P(A), needs to be computed first. That computation is shown in Equation 7-10. P(A) = P(AnC)+P(An•C) = P(C). P(AIC) + P( ·C). P(A I·C) = 0.01 X 0.95 + 0.99 X 0.06 = 0.0689 (7-10} According to Bayes' theorem, the probability of having the disease, given that Mary has a positive test result, is shown in Equation 7-11. P(CjA)= P(AIC)P(C) = 0.95x0.01 ::::::0.1379 P(A) 0.0689 (7-11) That means that the probability of Mary actually having the disease given a positive test result is only 13.79%. This result indicates that the lab test may not be a good one. The likelihood of having the disease ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION was 1% when the patient walked in the door and only 13.79% when the patient walked out, which would suggest further tests. A more general form of Bayes' theorem assigns a classified label to an object with multiple attributes A= {a1,a2 , ••. ,am} such that the label corresponds to the largest value of P(c1IA). The probability that a set of attribute values A (composed of m variables a1,a2 , ••• ,am) should be labeled with a classification label c1 equals the probability thatthe set of variables al' a2 , ••• , am given c1 is true, times the probability of c1 divided by the probability ofa1,a2 , •• • ,am. Mathematically, this is shown in Equation 7-12. P( lA) _ P(a 1 ,a 2 , ••• ,amlc 1 )·P(c) . _ 1 2 c. - ,1-, , ... n ' P(a 1 ,a 2 , ••• ,am) (7-12) Consider the bank marketing example presented in Section 7.1 on predicting if a customer would subscribe to a term deposit. LetA be a listofattributes{job, marital, education, default, housing, loan, contact,poutcome}. According to Equation 7-12, the problem is essentially to calculate P(c 1 IA), where c 1 E {subscribed= yes,subscribed =no}. 7.2.2 Na·ive Bayes Classifier With two simplifications, Bayes' theorem can be extended to become a na"ive Bayes classifier. The first simplification is to use the conditional independence assumption. That is, each attribute is conditionally independent of every other attribute given a class label 'r See Equation 7-13. m P(al'a 2 , •• • ,amlc1) = P(a1lc1)P(a2 lc1)···P(amlc1) = f]P(a1lc1) }=1 Therefore, this na'ive assumption simplifies the computation of P(al'a2, ••• ,amlc;l. (7-13) The second simplification is to ignore the denominatorP(a1,a2 , ••• ,am). BecauseP(a1,a2 , ... ,am) appears in the denominator of P(c 1 IA) for all values of i, removing the denominator will have no impact on the relative probability scores and will simplify calculations. Na"ive Bayes classification applies the two simplifications mentioned earlier and, as a result, P(c 1 la 1 ,a 2 , ••• ,am) is proportional to the product of P(a 1 lc 1 ) times P(c;). This is shown in Equation 7-14. m P(c1IA) 0< P(c1)· f1P(a1lc1) }=1 i = 1,2, ... n The mathematical symbol 0< indicates that the LHS P(c 1 IA) is directly proportional to the RHS. (7-14) 7.2 Na'ive Bayes Section 7.1 has introduced a bank marketing dataset (Figure 7-3). This section shows how to use the naiVe Bayes classifier on this dataset to predict if the clients would subscribe to a term deposit. Building a na'ive Bayes classifier requires knowing certain statistics, all calculated from the training set. The first requirement is to collect the probabilities of all class labels, P(c 1 ).1n the presented example, these would be the probability that a client will subscribe to the term deposit and the probability the client will not. From the data available in the training set, P( subscribed= yes)~ 0.11 and P( subscribed= no)~ 0.89. The second thing the na'ive Bayes classifier needs to know is the conditional probabilities of each attribute a 1 given each class label c 1 , namely P(a 1 lc1) • The training set contains several attributes: job, marital, education, default, housing, loan, contact,andpoutcome. For each attribute and its possible values, computing the conditional probabilities given subscribed= yes or subscribed= no is required. For example, relative to the marital attribute, the following conditional probabilities are calculated. P(single I subscribed= yes)~ 0.35 P(married I subscribed= yes)~ 0.53 P(divorced I subscribed= yes)~ 0.12 P(single I subscribed= no)~ 0.28 P(married I subscribed = no) ~ 0.61 P(divorced I subscribed= no)~ 0.11 After training the classifier and computing all the required statistics, the na'ive Bayes classifier can be tested over the testing set. For each record in the testing set, the na'ive Bayes classifier assigns the classifier label c1 that maximizesP(c1) ·Tij : 1 P(a1lc1). Table 7-4 contains a single record for a client who has a career in management, is married, holds a sec- ondary degree, has credit not in default, has a housing loan but no personal loans, prefers to be contacted via cellular, and whose outcome of the previous marketing campaign contact was a success. Is this client likely to subscribe to the term deposit? TABLE 7-4 Record of an Additional Client Job Marital Education Default Housing Loan Contact Poutcome management married secondary no yes no cellular Success ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION The conditional probabilities shown in Table 7-5 can be calculated after building the classifier with the training set. TABLE 7-5 Compute Conditional Probabilities for the New Record j a 1 P(a 1 I subscribed= yes) P(a 1 I subscribed=no) job= management 0.22 0.21 2 marital = married 0.53 0.61 3 education = 0.46 0.51 secondary 4 default = no 0.99 0.98 5 housing= yes 0.35 0.57 6 loa n = no 0.90 0.85 7 contact= cellular 0.85 0.62 8 poutcome= 0.15 0.01 success Beca use P(c 1 la 1 ,a 2 , .. . ,am) is proportional to the product of P(a 1 1c, )(j E [1,m]) times (c1), the na'ive Bayes classifier assigns the class label c 1 • which results in t he greatest value over all i. Thus, P(c1la1,a2 , . •• ,am) is computed for each c1 with P(c11Al P(c1) • IJ j : 1 P( a1lc1) • For A ={management, married, secondary, no, yes, no, cellular, success}, P(yesiA ) P(subscribed = nolA), the client shown in Table 7-4 is assigned with the
label subscribed = yes. That is, the client is classified as likely to subscribe to the term deposit.
Although the scores are small in magnitude, it is the ratio of P(yesiA ) and P(naiA ) that matters. In fact,
the scores of P( yesiA) and P( nolA) are not the true probabi lities but are only proportional to the true prob-
abilities, as shown in Equation 7-14. After all, if the scores were indeed the true probabilities, the sum of
P( yesiA) and P(noiA ) would be equal to one. When looking at problems with a large number of attributes,
or attri butes with a high number of levels, these va lues can become very smal l in magnit ude (close to zero),
resulting in even smaller differences of the scores. This is the problem of n umerical underflow, caused by
mu ltiplying several probability val ues that are close to zero. A way to alleviate the problem is to compute

7.2 Naive Bayes
the logarithm of the products, which is equivalent to the summation of the logarithm of the probabilities.
Thus, the na’ive Bayes formula can be rewritten as shown in Equation 7-15.
m
P(c;IA) ex: logP(c;}+ L:.::logP(ailc;)
j=l
i=1,2, … n (7-15}
Although the risk of underflow may increase as the number of attributes increases, the use of logarithms
is usually applied regardless of the number of attribute dimensions.
7.2.3 Smoothing
If one of the attribute values does not appear with one of the class labels within the training set, the
corresponding P(ailc;} will equal zero. When this happens, the resulting P(c;IA) from multiplying all the
P(ailc)(j E [1,m]} immediately becomes zero regardless of how large some of the conditional probabilities
are. Therefore overfitting occurs. Smoothing techniques can be employed to adjust the probabilities of
P(ailc;} and to ensure a nonzero value ofP(c;IA). A smoothing technique assigns a small nonzero probability
to rare events not included in the training dataset. Also, the smoothing addresses the possibility of taking
the logarithm of zero that may occur in Equation 7-15.
There are various smoothing techniques. Among them is the Laplace smoothing (or add-one} tech-
nique that pretends to see every outcome once more than it actually appears. This technique is shown in
Equation 7-16.
• count(x)+1
P (x)= E)count(x)+ 1]
(7-16}
For example, say that 100 clients subscribe to the term deposit, with 20 of them single, 70
married, and 10 divorced. The “raw” probability is P(singlelsubscribed=yes)=20/100=0.2.
With Laplace smoothing adding one to the counts, the adjusted probability becomes
P'(singlelsubscribed =yes)= (20+ 1}1[(20+ 1)+(70+ 1)+(10+ 1}] ~ 0.2039.
One problem of the Laplace smoothing is that it may assign too much probability to unseen events. To
address this problem, Laplace smoothing can be generalized to use£ instead of 1, where typically£ E [0, t
See Equation 7-17.
•• count( x) + £
P ( x) = -E-) c-o-un-t.,-( x.,–) +-~-=] (7-17)
Smoothing techniques are available in most standard software packages for na”ive Bayes classifiers.
However, if for some reason (like performance concerns) the na·ive Bayes classifier needs to be coded
directly into an application, the smoothing and logarithm calculations should be incorporated into the
implementation.
7.2.4 Diagnostics
Unlike logistic regression, na’ive Bayes classifiers can handle missing values. Na”ive Bayes is also robust to
irrelevant variables-variables that are distributed among all the classes whose effects are not pronounced.

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
The model is simple to implement even without using libraries. The prediction is based on counting the
occurrences of events, making the classifier efficient to run. Na’ive Bayes is computationally efficient and is
able to handle high-dimensional data efficiently. Related research [13] shows that the naive Bayes classifier
in many cases is competitive with other learning algorithms, including decision trees and neural networks.
In some cases na”ive Bayes even outperforms other methods. Unlike logistic regression, the na”ive Bayes
classifier can handle categorical variables with many levels. Recall that decision trees can handle categorical
variables as well, but too many levels may result in a deep tree. The na”ive Bayes classifier overall performs
better than decision trees on categorical values with many levels. Compared to decision trees, na’ive Bayes
is more resistant to overfitting, especially with the presence of a smoothing technique.
Despite the benefits of na”ive Bayes, it also comes with a few disadvantages. Na”ive Bayes assumes the
variables in the data are conditionally independent. Therefore, it is sensitive to correlated variables because
the algorithm may double count the effects. As an example, assume that people with low income and low
credit tend to default. If the task is to score “default” based on both income and credit as two separate
attributes, na’ive Bayes would experience the double-counting effect on the default outcome, thus reduc-
ing the accuracy of the prediction.
Although probabilities are provided as part of the output for the prediction, na”ive Bayes classifiers in
general are not very reliable for probability estimation and should be used only for assigning class labels.
Na’ive Bayes in its simple form is used only with categorical variables. Any continuous variables should be
converted into a categorical variable with the process known as discretization, as shown earlier. In com-
mon statistical software packages, however, na”ive Bayes is implemented in a way that enables it to handle
continuous variables as well.
7.2.5 Na·ive Bayes in R
This section explores two methods of using the na’ive Bayes classifier in R. The first method is to build from
scratch by manually computing the probability scores, and the second method is to use the nai veBayes
function from the el o 71 package. The examples show how to use na’ive Bayes to predict whether employ-
ees would enroll in an onsite educational program.
In R, first set up the working directory and initialize the packages.
setwd ( “c: I”)
install.packages(“el071”) # install pac~age e1071
library(el071) # load the library
The working directory contains a CSV file (samplel. csv). The file has a header row, followed by 14
rows of training data. The attributes include Age, Income, JobSatisfaction, and Desire. The
output variable is Enrolls, and its value is either Yes or No. Full content of the CSV file is shown next.
Age,Income,JobSatisfaction,Desire,Enrolls
<=30,High,No,Fair,No <=30,High,No,Excellent,No 31 to 40,High,No,Fair,Yes >40,Medium,No,Fai~.Yes
>40,Low,Yes,Fai~.Yes
>40,Low,Yes,Excellent,No
31 to 40,Low,Yes,Excellent,Yes

<=30,Medium,No,Fair,No <=30,Low,Yes,Fair,Yes >40,Medium,Yes,Fair,Yes
<=30,Medium,Yes,Excellent,Yes 31 to 40,Medium,No,Excellent,Yes 31 to 40,High,Yes,Fair,Yes >40,Medium,No,Excellent,No
<=30,Medium,Yes,Fair, 7.2 Na'ive Bayes The last record of the CSV is used later for illustrative purposes as a test case. Therefore, it does not include a value for the output variable Enrolls, which should be predicted using the na'ive Bayes classifier built from the training set. Execute the following R code to read data from the CSV file. # read the data into a table from the file sample <- read. table ( 11 samplel . csv n , header= TRUE, sep= 11 , " ) # define the data frames for the NB classifier traindata <- as.data.frame(sample[l:l4,]) testdata <- as.data.frame(sample[lS,]) Two data frame objects called trainda ta and test data are created for the na'ive Bayes classifier. Enter traindata and testdata to display the data frames. The two data frames are printed on the screen as follows. traindata Age Income JobSatisfaction Desire Enrolls 1 <=30 High No Fai~ No 2 <=30 High No Excellent No 3 31 to 40 High No Fai:.- Yes 4 >40 i-ledi urn No Fai:.- Yes
5 >40 Lo~,o: Yes Fai:- Yes
6 >40 Low Yes Excellent No
7 31 to 40 Low “les Excellent Yes
8 <=30 f.lediurn No Fai:- No 9 <=30 Low Yes Fai:- Yes 10 >40 Medium Yes Fah· Yes
11 <=30 Ivledium Yes Excellent Yes 12 31 to 40 Medium No Excellent Yes 13 31 to 40 High Yes Fair· Yes 14 >40 1-!ediurn No Excellent No
testdata
Age Income JobSatisfaction Cesire Enrolls
15 <=30 !•ledium Yes Fair The first method shown here is to build a na'ive Bayes classifier from scratch by manually computing the probability scores. The first step in building a classifier is to compute the prior probabilities ofthe attributes, ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION including Age, Income, JobSatisfaction, and Desire. According to the naive Bayes classifier, these attributes are conditionally independent. The dependent variable (output variable) is Enrolls. Compute the prior probabilities P(c;) for Enrolls, where c; E C and C = {Yes,No}. tprior <- table(traindata$Enrolls) tprior No Yes tprior <- tprior/sum(tprior) tprior No Yes 0.0000000 0.3571429 0.6428571 The next step is to compute conditional probabilitiesP(AIC), where A ={Age, Income, JobSatisfaction,Desire} and C ={Yes, No}. Count the number of "No" and "Yes" entries for each Age group, and normalize by the total number of"No" and "Yes" entries to get the conditional probabilities. ageCounts <- table (traindata [, c ("Enrolls", "Agen)]) age Counts f1.ge ageCounts <- ageCounts/rowSums(ageCounts) ageCounts Age Em.,olls <=30 >40 31 ::o 40
No 0.6000000 0.4000000 0.0000000
Yes 0.2222222 0.3333333 0.44~444~
Do the same for the other attributes including Income, JobSatisfaction, and Desire.
incomeCounts <- table (traindata [, c ( 11 Enrolls", "Income 11 )]) incomeCounts <- incomeCounts/rowSums(incomeCounts) incomecounts Yes 0.2222222 0.3333333 .4444444 jsCounts <- table(traindata[,c("Enrolls", "JobSatisfaction")]) jsCounts <- jsCounts/rowSums(jsCounts) jsCounts Jobsatisfaction Enrolls No Yes No o.soooooo o.~oooca Yes 0.3333333 0.6GGG667 desireCounts <- table (traindata [, c ("Enrolls", "Desire'•) 1) desireCounts <- desireCounts/rowSums(desireCounts) desireCounts Desire Enrolls Exce:lent Fair No 0.60000CO 8.~0080CO Yes 0.3333333 O.E66GGG7 7.2 Na'ive Bayes According to Equation 7-7, probability P(c1IA) is determined by the product of P(ajlc,) times the (c1) where c 1 =Yes and c 2 =No. The larger value of P(YesiA) and P(NoiA) determines the predicted result of the output variable. Given the test data, use the following code to predict the Enrolls. prob_yes <- agecounts [ '1Yes 11 , testdata [, c ("Age 11 ) 1 1 * incomeCounts ["Yes 11 , testdata [,c ( 11 Income'1 ) 11 * j sCounts [ 11 Yes ., , testdata [, c ( '1JobSatisfaction 11 ) 1 1 * desireCounts [ 11 Yes", testdata [, c ( '1Desire 11 ) 1 1 * tprior [ 11 Yes 11 1 prob_no <- ageCounts [ 11 No", testdata [, c ( '1Age") 11 * incomeCounts ["No 11 , testdata [, c ( 11 Income 11 ) 1 1 * jsCounts ["No", testdata [, c ( 11 JobSatisfaction") 1 1 * desireCounts [ "No'•, testdata [, c ( "Desire 11 ) 1 1 * tprior["No"1 max(prob_yes,prob_no) As shown below, the predicted result of the test set is Enrolls= Yes. prob_yes Yes 0.02821869 prob_no No 0.006857:..43 max(prob_yes, prob_no) [l! 0.0282l869 ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION Theel o 71 package in R has a built-in nai veBayes function that can compute the conditional probabilities of a categorical class variable given independent categorical predictor variables using the Bayes rule. The function takes the form of nai veBayes (formula I data I ••• ) I where the argu- ments are defined as follows. o formula: A formula ofthe form class - xl + x2 + ... assuming xll x2 ... are conditionally independent o data: A data frame of factors Use the following code snippet to execute the model and display the results. model <- naiveBayes(Enrolls - Age+Income+JobSatisfaction+Desire 1 traindata) # display model model The output that follows shows that the probabilities of model match the probabilities from the previous method. The default laplace= laplace setting enables the Laplace smoothing. Naive Bayes Classif~er f2r Discrete Predictors Call: naiveBayes.default'v '!, laplace laplace, h-priori probabilit~es: y No ''les 0.0000000 0.35714~9 0.6428571 Conditional probabilities: P..ge y >40 31 to 40
No 0.6000000 0.4000000 0.0000000
Yes 0. 2222222 0. 33 3333~ 0 . •l444•l4’l
Income
y High LO’d i•ledium
No 0 .4000000 C.20000,’JO ·:J . ·l 0 0 🙂 0 0 0
“/es 0.2222222 0 3 3 3 3 3 3 ~ 0 .. J.;4444·l
~obSatisfac:ion
“l No Y•:;:”
No 0 8 0 8• ()I~ 0 c ~ ] ~c~’ ·:: 0 ;J
‘£es 0.3333333 il.((GhGE7

y
Desire
Excellent Fair
No 0.6000000 0.40COOOO
Yes 0.3333333 0.6666667
7.2 Na’ive Bayes
Next, predicting the outcome of Enrolls with the t es tda t a shows the result is Enrolls= Yes.
# predict with testdata
results <- predict (model,testdata) # display results results [l] Yes Levels: ~~o Yes The nai veBayes function accepts a Laplace parameter that allows the customization of the c value of Equation 7-17 for the Laplace smoothing. The code that follows shows how to build a na"ive Bayes clas- sifier with Laplace smoothing c = 0.01 for prediction. # use the NB classifier with Laplace smoothing modell = naiveBayes(Enrolls -., traindata, laplace=.Ol) # display model modell Naive Bayes Classifier for ~~scre~e ?redictcrs Call: naiveaayes.defaul:,v Z, y h-priori probabilities: y 0.0000000 0.3571429 C.642857l Conditional probabilities: .i'-.ge y < = 3 C: >~ ‘=
0.333333333 0 333333332
No 0 598409543 0 399602386
Yes 0.222591362 0 ::33333333
Inco:ne
y High Low
Y, laplace
Jl to .;o
0 333333333
0 00198807:.2
0 .444075305
t·ledium
0.3333333 0.3333333 O.l333333
No 0.3996024 0.2007952 0.3996024
Yes 0.2225914 0.33:33333 0.4440753
laplace1

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
y
r:
The test case is again classified as Enroll s=Yes.
:: predirt ·.·:.:..~”” .. · ~ d:-1 .. ~
results! <- predict (modell,testdata ) results! ~ v ... 7.3 Diagnostics of Classifiers So far, this book has talked about three classifi ers: logistic reg ression, decision trees, and na"lve Bayes. These three methods can be used to classify instances into distinct groups according to the similar characteris tics they share. Each of these classifiers faces the same issue: how to evaluate if they perform well. A few tools have been desig ned to evaluate the performance of a classifier. Such tools are not limited to th e three classifiers in this book but rather serve the purpose of assessing cla ssifiers in genera l. A confusion matrix is a specifi c table layout that allows visualization of the performance of a classifier. Table 7-6 shows the confusion matrix for a two -class classifier. True positives (TP) are the number of positive insta nces the classifier correctly identified as positive. False positives (FP) are the number of instances in which the cl assifier identified as positive but in reality are negative. True negatives (TN) are the number of negative instances the classifier correctly identified as negative. False negatives (FN) are the num ber of instanc es classified as negative but in reality are positive. In a two-class classification, a preset threshold may be used to sepa rate positives from negatives. TP and TN are the correct guesses. A good classifier should have large TP and TN and small (ideally zero) numbers for FP and FN. TABLE 7 6 Confusion Matrix True Positives (TP) False Negatives (FN) False Positives (FP) True Negatives (TN) 7.3 Diagnostics of Classifiers In the bank marketing example, the training set includes 2,000 instances. An additional100 instances are included as the testing set. Table 7-7 shows the confusion matrix of a na"ive Bayes classifier on 100 clients to predict whether they would subscribe to the term deposit. Of the 11 clients who subscribed to the term deposit, the model predicted 3 subscribed and 8 not subscribed. Similarly, of the 89 clients who did not subscribe to the term, the model predicted 2 subscribed and 87 not subscribed. All correct guesses are located from top left to bottom right of the table. It's easy to visually inspect the table for errors, because they will be represented by any nonzero values outside the diagonal. TABLE 7-7 Confusion Matrix of Naive Bayes from the Bank Marketing Example The accuracy {or the overall success rate) is a metric defining the rate at which a model has classified the records correctly. It is defined as the sum ofTP and TN divided by the total number of instances, as shown in Equation 7-18. TP+TN Accuracy= x 100% TP+TN+FP+FN (7-18) A good model should have a high accuracy score, but having a high accuracy score alone does not guarantee the model is well established. The following measures can be introduced to better evaluate the performance of a classifier. As seen in Chapter 6, the true positive rate (TPR) shows what percent of positive instances the classifier correctly identified. It's also illustrated in Equation 7-19. TPR = ____!!!___ TP+FN (7-19) The false positive rate {FPR) shows what percent of negatives the classifier marked as positive. The FPR is also called the false alarm rate or the type I error rate and is shown in Equation 7-20. FPR=_!!_ FP+TN (7-20) The false negative rate (FNR) shows what percent of positives the classifier marked as negatives. It is also known as the miss rate or type II error rate and is shown in Equation 7-21. Note that the sum of TPR and FNR is 1. FNR=__!!!_ TP+FN {7-21) ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION A well-performed model should have a high TPR that is ideally 1 and a low FPR and FNR that are ideally 0. In reality, it's rare to have TPR = 1, FPR = 0, and FNR = 0, but these measures are useful to compare the perfor- mance of multiple models that are designed for solving the same problem. Note that in general, the model that is more preferable may depend on the business situation. During the discovery phase of the data analytics lifecycle, the team should have learned from the business what kind of errors can be tolerated. Some business situations are more tolerant of type I errors, whereas others may be more tolerant of type II errors. In some cases, a model with a TPR of 0.95 and an FPR of 0.3 is more acceptable than a model with a TPR of 0.9 and an FPR of 0.1 even if the second model is more accurate overall. Consider the case of e-mail spam filtering. Some people (such as busy executives) only want important e-mail in their inbox and are tolerant of having some less important e-mail end up in their spam folder as long as no spam is in their inbox. Other people may not want any important or less important e-mail to be specified as spam and are willing to have some spam in their in boxes as long as no important e-mail makes it into the spam folder. Precision and recall are accuracy metrics used by the information retrieval community, but they can be used to characterize classifiers in general. Precision is the percentage of instances marked positive that really are positive, as shown in Equation 7-22. , . . TP rfeCISIOn = --- TP+fP (7-22) Recall is the percentage of positive instances that were correctly identified. Recall is equivalent to the TPR. Chapter 9, "Advanced Analytical Theory and Methods: Text Analysis," discusses how to use precision and recall for evaluation of classifiers in the context of text analysis. Given the confusion matrix from Table 7-7, the metrics can be calculated as follows: TP+TN 3+87 Accuracy= x 1 00% = x 100% = 90% TP+ TN +FP+FN 3+87 +2+8 TP 3 TPR (or Recall)=--=--~ 0.273 TP+FN 3+8 FPR = _FP_ = - 2- ~ 0.022 FP+TN 2+87 FNR = _FN_ = - 8- ~ 0.727 TP+FN 3+8 , . . TP 3 06 rreCISIOn = --- = -- = . TP+FP 3+2 These metrics show that for the bank marketing example, the na"ive Bayes classifier performs well with accuracy and FPR measures and relatively well on precision. However, it performs poorly on TPR and FNR. To improve the performance, try to include more attributes in the datasets to better distinguish the characteristics of the records. There are other ways to evaluate the performance of a classifier in general, such as N-fold cross validation (Chapter 6) or bootstrap [14]. Chapter 6 has introduced the ROC curve, which is a common tool to evaluate classifiers. The abbre- viation stands for receiver operating characteristic, a term used in signal detection to characterize the trade-off between hit rate and false-alarm rate over a noisy channel. A ROC curve evaluates the performance of a classifier based on the TP and FP, regardless of other factors such as class distribution and error costs. The vertical axis is the True Positive Rate (TPR), and the horizontal axis is the False Positive Rate (FPR). 7.3 Diagnostics of Classifiers As seen in Chapter 6, any classifier can achieve the bottom left of the graph where TPR = FPR = 0 by classifying everything as negative. Similarly, any classifier can achieve the top right of the graph where TPR = FPR = 1 by classifying everything as positive. If a classifier performs "at chance" by random guessing the results, it can achieve any point on the diagonal line TPR=FPR by choosing an appropriate threshold of positive/negative. An ideal classifier should perfectly separate positives from negatives and thus achieve the top-left corner (TPR = 1, FPR = 0). The ROC curve of such classifiers goes straight up from TPR = FPR = 0 to the top-left corner and moves straight right to the top-right corner. In reality, it can be difficult to achieve the top-left corner. But a better classifier should be closer to the top left, separating it from other classifiers that are closer to the diagonal line. Related to the ROC curve is the area under the curve (AUC). The AUC is calculated by measuring the area under the ROC curve. Higher AUC scores mean the classifier performs better. The score can range from 0.5 (for the diagonal line TPR=FPR) to 1.0 (with ROC passing through the top-left corner). In the bank marketing example, the training set includes 2,000 instances. An additional100 instances are included as the testing set. Figure 7-10 shows a ROC curve ofthe na'ive Bayes classifier built on the training set of 2,000 instances and tested on the testing set of 100 instances. The figure is generated by the following R script. The ROCR package is required for plotting the ROC curve. The 2,000 instances are in a data frame called bank train, and the additional100 instances are in a data frame called bank test. library (ROCR) # training set banktrain <- read.table("bank-sample.csv",header=TRUE,sep=",") # drop a few columns drops<- c("balance", "day", "campaign", "pdays", "previous 11 , "month") banktrain <- banktrain [,! (names(banktrain) %in% drops)] # testing set banktest <- read.table("bank-sample-test.csv",header=TRUE,sep= 11 , 11 ) banktest <- banktest [,! (names(banktest) %in% drops)] # build the naive Bayes classifier nb_model <- naiveBayes(subscribed-., data=banktrain) # perform on the testing set nb_prediction <- predict(nb_model, # remove column "subscribed" banktest[,-ncol(banktest)], type=' raw' ) score <- nb _prediction [, c ("yes") 1 actual_class <- banktest$subscribed == 'yes' pred <- prediction(score, actual_class) ADVA NCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION perf <- performance (pred, "tpr" , " fpr " ) plot(perf, lwd=2, xlab="False Positive Rate (FPR) ", ylab="True Positive Rate (TPR) " ) abline (a=O, b=l, col="gray50" , lty=3 ) The following R code shows that the corresponding AUC score of the ROC cur ve is about 0.915. auc < - performance (pred, "auc" ) auc <- unlist (slot (auc, "y . values" )) auc 0 (X) [ 0 t:. "' U) 1;j 0 a: "' > Q …. ill
0 0
Q.
“‘ 2 N …. 0
0
0
00 02 04 06
False PoSlOve Rate (FPR)
08 10
FIGURE 7 10 ROC curve of the naive Bayes classifier on the bank marketing dataset
7.4 Additional Classification Methods
Besides the two classifiers introduced in this chapter, several other methods are commonly used for clas-
sification, includi ng bagging [15], boosting [5], random forest [4], and support vector machines (SVM) [16].
Bagging, boosti ng, and random forest are all examples of ensemble methods that use multiple models to
obtain better predictive performance than can be obtained from any of the constituent models.
Bagging (or bootstrap aggregating) [15] uses the bootstrap technique that repeatedly samples with
replacement from a dataset accord ing to a uniform probability distribution. “With replacement” mea ns
that when a sam ple is selected for a training or testing set, t he sample is still kept in the dataset and may
be selected again. Because the sampling is with replacement, some sam ples may appear several times in
a training or testi ng set, whereas others may be absent. A model or base classifi er is trained separately on
each bootstrap sample, and a test sample is assigned to the cl ass t hat received the highest number of votes.
Similar to bagging, boosting (or Ada Boost) [17] uses votes for classi fi cation to combine the output of indi-
vidual models. In addition, it combines models of the same type. However, boosting is an iterative procedure

Sum mary
where a new model is influenced by the performances of those models built previously. Furthermore,
boosting assigns a weight to each training sample t hat reflects its importance, and the weight may adap-
tively change at the end of each boosting round. Bagging and boosting have been shown to have better
performances [5) than a decision tree.
Random forest [4] is a class of ensemble methods using decision tree classifiers. It is a combination of tree
predictors such that each tree depends on the values of a random vector sampled independently and with
the same distribution for all trees in the forest. A special case of random forest uses bagging on decision
t rees, where samples are randomly chosen with replacement from the original training set.
SVM [16] is another common classification method that combines linear models with instance-based
learning techniques. Support vector machines select a small number of critical boundary instances cal led
sup port vec t ors fr om each cla ss and build a lin ea r decision function that separates them as widely as
possible. SVM by default can efficiently perform linear classifications and can be configured to perform
nonlinear classifications as well.
Summary
This chapter focused on two cl assification methods: decision trees and na’ive Bayes. It discussed the theory
behind these classifiers and used a bank marketing exa mple to explain how the methods work in practice.
These classifiers along with logistic regression (Chapter 6) are often used for the classification of data. As
this b ook has discussed, each of t hese methods has it s own advantages and disadvantages. How does one
pick t he most suit able method for a given classification problem? Table 7-8 offers a list of things to consider
when select ing a classifier.
TABLE 7·8 Choosing a Suitable Classifier
Concerns Recommended Method(s)
Output ofthe classification should include class Logistic regression, decision tree
probabilities in addition to the class labels.
Analysts wan t to gain an insight into how the vari- Logistic reg ression, decision tree
abies affect the model.
The problem is h igh dimensional. Na’ive Bayes
Some of the in put variables might be correlated. Logistic regression, decision tree
Some of the input va ri ables might be irrelevant. Decision tree, na’ive Bayes
The data contains categori cal variables with a Decision tree, na’ive Bayes
large number of levels.
The data contains mixed vari able types. Log istic reg ression, decision tree
There is nonlinear dat a or discontinuities in the Decision tree
input va riables that would affect the output.

ADVANCED ANALYTICAL THEORY AND METHODS: CLASSIFICATION
After the classification, one can use a few evaluation tools to measure how well a classifier has performed
or compare the performances of multiple classifiers. These tools include confusion matrix, TPR, FPR, FNR,
precision, recall, ROC curves, and AUC.
In add ition to the decision trees and na’ive Bayes, other methods are commonly used as classifiers. These
methods include but are not limited to bagging, boosting, random forest, and SVM.
Exercises
1. For a bina ry classification, describe the possible values of entropy. On what conditions does
entropy reach its minimum and maximum values?
2. In a decision tree, how does the algorithm pick the attributes for splitting?
3. John went to see the doctor about a severe headache. The doctor selected John at random to
have a blood test for swine nu, which is suspected to affect 1 in 5,000 people in this country. The
test is 99% accurate, in the sense that the probability of a false positive is 1%. The probability of a
false negative is zero. John’s test came back positive. What is the probability that John has swine
flu?
4. Which classifier is considered computationally efficient for high-dimensional problems? Why?
5. A data science team is working on a classification problem in which the dataset contains many
correlated variables, and most of them are categorical variables. Which classifier should the team
consider using? Why?
6. A data science team is working on a classification problem in which the dataset contains many
correlated variables, and most of them are continuous. The team wa nts the model to output the
probabilities in addition to the class labels. Which classifier should the team consider using? Why?
7. Consider the following confusion matrix:
What are the true positi ve rate, false positive rate, and false negative rate?

Bibliography
Bibliography
[1] M. Thomas, B. Pang, and L. Lee, “Get Out the Vote: Determining Support or Opposition from
Congressional Floor-Debate Transcripts,” in Proceedings of th e 2006 Conference on Empirical
Methods in Natural Language Processing, Sydney, Aus tralia, 2006.
[2) M. Shouman, T. Turner, and R. Stocker, “Using Decision Tree for Diagnosing Heart Disease Patients,”
in Australian Computer Society, Inc., Ballarat, Australia, in Proceedings of the Ninth Australasian
Data Mining Conference (AusDM ’11).
[3) I. Androutsopoulos, J. Koutsias, K. V. Chandrinos, G. Paliouras, and C. D. Spyropoulos, “An Evaluation
of Na’fve Bayesian Anti-Spam Fi ltering,” in Proceedings of the Workshop on Machine Learning in
the New Information Age, Barcelona, Spai n, 2000.
[4) L. Brei man, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5- 32, 2001 .
[5] J. R. Quin lan, “Bagg ing, Boosting, and C4. 5,” AAAI/IAAI, vol. 1, 1996.
[6) S. Moro, P. Cortez, and R. Laureano, “Using Data Mini ng for Bank Direct Marketing: An Application
of the CRISP-DM Methodology,” in Proceedings of the European Simulation and Modelling
Conference- ESM’2011, Guimaraes, Portugal, 2011.
[7) J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[8) J. R. Quinlan, C4. 5: Programs for Machine Learning, Morgan Kaufma nn, 1993.
[9) L. Brei man, J. H. Friedma n, R. A. Olshen, and C. J. Stone, Classification and Regression Trees,
Belmont, CA: Wadsworth Internationa l Group, 1984.
[10) T. M. Mitchell, “Decision Tree Learning,” in Machine Learning, New York, NY, USA, McGraw-H ill, Inc.,
1997, p. 68.
[11) C. Phua, V. C. S. Lee, S. Kate, and R. W. Gayler, “A Comprehensive Survey of Data Mining-Based Fraud
Detection,” CaRR, vol. abs/1009.6119, 2010.
[12) R. Bhowmik, “Detecting Auto Insurance Fraud by Data Mining Techniques,” Journal of Emerging
Trends in Computing and Information Sciences, vol. 2, no. 4, pp. 156- 162,2011.
[13) D. Michie, D. J. Spiegel halter, and C. C. Taylor, Machine Learning, Neural and Statistical
Classification, New York: Ell is Horwood, 1994.
[14) I. H. Witten, E. Frank, and M. A. Hall, ” The Bootstra p,” in Data Mining, Burlington, Massachusetts,
Morgan Kaufmann, 2011 , pp. 155-156.
[15) L. Breiman, “Bagging Predictors,” Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
[16) N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-
Based Learning Methods, Cambridge, United Kingdom: Cambridge university press, 2000.
[17) Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an
Application to Boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.

ADVANCED ANALYTICA L THEORY AND METHODS: TIME SER IE S ANA LYSIS
This chapter examines the topic of time seri es analysis and its applications. Emphasis is placed on identifying
the underlying structure of the time seri es and fitting an appropriate Autoregressive Integrated Moving
Average (ARIMA) model.
8.1 Overview of Time Series Analysis
Time seri es analysis attempts to model the underlying structure of observations taken over time. A time
series, denoted Y = a + bX , is an ordered sequence of equally spaced values over time. For example,
Figure 8-1 provides a plot of the monthly number of international airline passengers over a 12-year peri od.
u;-
‘0
c
ro
0 V>
::J 0
0 I()
§.
V>
iii 0 0)
c 0
Q)
(“)
V>
V>
ro
0..
~ 0
< 0 0 20 40 60 80 100 120 140 Time (Monlhs) FIGURE 8-1 Monthly in ternational airline passengers In this exampl e, the time series consists of an ordered sequence of 144 values. The analyses presented in this cha pter are limited to equally spaced time series of one variable. Following are the goa ls of time seri es analysis: • Identify and model t he structure of the time series. • Forecast future values in the time series. Time series analysis has many applications in finance, economics, biology, engineering, retai l, and manufacturing. Here are a few specific use cases: • Re tai l sa les: For various product lines, a clothing retailer is looking to forecast future month ly sa les. These forecasts need to account for the seasonal aspects of the customer's purchasing decisions. For example, in the northern hemisphere, sweater sales are typically brisk in the fall season, and swimsuit sales are the hig hest during the late spring and early summer. Thu s, an appropriate time series model needs to account for fluctuating demand over the ca lendar year. • Spare parts p la nni ng: Companies' servi ce organizations have to forecast future spare pa rt demands to ensure an adequate supply of parts to repair customer products. Often the spares inven- tory consists of thousands of distinct part numbers. To forecast future demand, complex models for each part number can be built using input variables such as expected part failure rates, service diag- nostic effectiveness, forecasted new product shipments, and forecasted trade-ins/decommissions. 8.1 Overview ofTime Series Analysi s However, time series analysis can provide accurate short-term forecasts based simply on prior spare part demand history. • Stock trading : Some high-frequency stock traders utilize a technique called pairs trading. In pairs trading, an identified strong positive correlation between the prices of two stocks is used to detect a market opportunity. Suppose the stock prices of Company A and Company B consistently move together. Time series analysis can be applied to the difference of these companies' stock prices over time. A statistica lly larger than expected price difference indicates that it is a good time to buy the stock of Company A and sell the stock of Company B, or vice versa. Of course, th is trading approach depends on t he abi lity to execute the trade quickly and be able to detect when the correlation in the stock prices is broken. Pairs trading is one of many techniques that fa lls into a trading strategy called statistical arbitrage. 8.1 .1 Box-Jenkins Methodology In this chapter, a time series consists of an ordered sequence of equally spaced values over time. Examples of a time series are monthly unemployment rates, daily website visits, or stock prices every second. A time series can consist of the following components: • Trend • Seasonality • Cyclic • Random The trend refers to the long-term movement in a time series. It indicates whether the observation values are increasi ng or decreasing over t ime. Examples of trends are a steady increase in sales month over month or an annual decline of fata lities due to ca r accidents. The seasonality component describes the fixed, periodic fluctuation in the observations over time. As the name suggests, the seasonality component is often related to the calendar. For example, monthly retail sales can fluctuate over the year due to the weather and holidays. A cyclic component also refers to a periodic fluctuation, but one that is not as fixed as in the case of a seasonality component. For example, retails sales are influenced by the general state of the economy. Thus, a retail sales time series can often follow the lengthy boom-bust cycles of the economy. After accounting for the other three components, the random component is what remains. Although noise is certainly part of this random component, there is often some underlying structure to this random component that needs to be modeled to forecast future values of a given time series. Developed by George Box and Gwilym Jenkins, the Box-Jenkins methodology for time series analysis involves the following three main steps: 1. Condition data and select a model. • Identify and account for any trends or seasonality in the time series. • Examine the rema ining time series and determine a suitable model. 2. Estimate t he model parameters. 3. Assess the model and return to Step 1, if necessary. ADVANCED ANALYTICAL THEORY AND METHODS: TIME SERIES ANALYSIS The primary focus of this chapter is to use the Box-Jenkins methodology to apply an A RIMA model to a given time series. To fully explain an ARIMA (Autoregressive Integrated Moving Average) model, this section describes the model's various parts and how they are combined . As stated in the first step of the Box-Jenkins methodol- ogy, it is necessary to remove any trends or seasonality in the time series. This step is necessa ry to achieve a time series with certain properties to whic h autoregressive and moving average models can be appl ied. Such a time series is known as a stationary time series. A time series, Y, fort = 1,2,3, ... ,, is a stationary time series if the following three conditions are met: (a) The expected value (mean) of Y, is a constant for all values oft. (b) The variance of Y, is finite. • (c) The covariance of Y, andY, h depends only on the value of h = 0, 1, 2, .. .for all t. The covariance of Y, andY,. his a measure of how the two variables, Y, andY, _ h• vary together. It is expressed in Equation 8-1. (8-1) If two variables are independent of each other, their covariance is zero. If the vari ables change together in the same direction, the variables have a positive covariance. Conversely, if the variables change together in the opposite direction, the variables have a negative covariance. For a stationa ry time seri es, by condition (a), the mean is a constant, say''· So, for a given stationary sequence, Y,. the covariance notation can be simplified to what's shown in Equation 8-2. (8-2) By part (c), the covariance between two points in the time series can be nonzero, as long as the value of the covariance is only a functi on of h. Equation 8-3 is an exa mple for h = 3. (8-3) It is imp ortant to note th at for h 0, the cov(O) = cov(y,,y,) = var(y,) fo r all t. Because t he var (y,) < , by condition (b), the variance of Y, is a constant for all t. So the constant variance coupled with part (a), E [y,] = I'· for all t and some constant 1', suggests that a stationary time series can look like Figure 8-2. In this plot, the points appear to be centered about a fixed constant, zero, and the variance appears to be somewhat constant over time. 8.2.1 Autocorrelation Function (ACF) Although there is not an overall trend in the time series plotted in Figure 8-2, it appears that each point is somewhat dependent on the past points. The difficulty is that the plot does not provide insight into the covariance of the variables in the time series and its underlying structure. The plot of autocorrelation function (ACF) provides this insight. For a stationary time series, the ACF is defined as shown in Equation 8-4. 8.2 ARIMA Model ACF ( h)= cov(y, ,y, . h) cov(h) Jcov (y,, Y, ) cov (y, . h, Y, h) cov(O) (8-4) 0 100 200 300 400 500 600 Time fiGURE 8· A plot of a stationary series Because the cov(O) is the variance, the ACF is analogous to the correlation function of two variables, carr (y,, Y, , h), and the value of the ACF falls between - 1 and 1. Thus, the closer the absolute value of ACF(h) is to 1, the more useful Y, can be as a predictor of Y, . h . Usi ng the sa me dataset plotted in Figure 8-2, the plot of the ACF is provided in Figure 8-3. ~ - CD c) - u.. (() 0 c) , Y,_. +t/>2 Yr-2 + … +¢ P Yr-p
+ g I + 91 g I_ 1 + … + {Jq g I_ q
where 15 is a constant for a nonzero-centered time series
4> i is a constant for j = 1, 2, … , p
tj>P=;e.O
(}*is a constant fork= 1, 2, … , q
oq =;e.O
c
1
– N (0, a~) for all t
(8-15)
If p = 0 and q =;e. 0, then the ARMA(p,q) model is simply an AR(p) model. Similarly, if p = 0 and q =;e. 0,
then the ARMA(p,q) model is an MA(q) model.
To apply an ARMA model properly, the time series must be a stationary one. However, many time
series exhibit some trend over time. Figure 8-7 illustrates a time series with an increasing linear trend
over time. Since such a time series does not meet the requirement of a constant expected value (mean),
the data needs to be adjusted to remove the trend. One transformation option is to perform a regres-
sion analysis on the time series and then to subtract the value of the fitted regression line from each
observed y-value.
If detrending using a linear or higher order regression model does not provide a stationary series, a
second option is to compute the difference between successive y-values. This is known as differencing.
In other words, for the n values in a given time series compute the differences as shown in Equation 8-16.
d
1
= Y,- y
1
_
1
fort=2,3, … ,n (8-16)