Machine Learning
I have this Assignment using Jupyter by Anaconda and Python
it’s due this Friday 13 please read the pdf file carefully Thanks
Assignment 5: Multi-Classification
Due date: Mar 13th, 2020 (Friday)
Total Points: 100
Please put your name, student ID, date and time here
Name:
Student ID:
Date:
Time:
In this assignment, you will investigate the handwritten digits dataset.
Sample images:
Please apply the folowing eight methods to classify the handwritten digits dataset.
Split the dataset into training sets and test sets
Fit the training data sets to the following eight algorithms
Print the classification report on the test data sets
Method 1: KNN
Method 2: Linear SVM
Method 3: Gaussian Kernel SVM
Method 4: Naive Bayes
Method 5: Decision Tree
Method 6: Random Forest
Method 7: Voting Classifier
Method 8: Bagging
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment 5.html
1 of 5 3/11/2020, 6:57 PM
In [4]: # Importing the dataset
from sklearn.datasets import load_digits
digits = load_digits()
print(digits)
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment 5.html
2 of 5 3/11/2020, 6:57 PM
{‘data’: array([[ 0., 0., 5., …, 0., 0., 0.],
[ 0., 0., 0., …, 10., 0., 0.],
[ 0., 0., 0., …, 16., 9., 0.],
…,
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 2., …, 12., 0., 0.],
[ 0., 0., 10., …, 12., 1., 0.]]), ‘target’: array([0, 1, 2, …, 8,
9, 8]), ‘target_names’: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), ‘images’: array
([[[ 0., 0., 5., …, 1., 0., 0.],
[ 0., 0., 13., …, 15., 5., 0.],
[ 0., 3., 15., …, 11., 8., 0.],
…,
[ 0., 4., 11., …, 12., 7., 0.],
[ 0., 2., 14., …, 12., 0., 0.],
[ 0., 0., 6., …, 0., 0., 0.]],
[[ 0., 0., 0., …, 5., 0., 0.],
[ 0., 0., 0., …, 9., 0., 0.],
[ 0., 0., 3., …, 6., 0., 0.],
…,
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 0., …, 10., 0., 0.]],
[[ 0., 0., 0., …, 12., 0., 0.],
[ 0., 0., 3., …, 14., 0., 0.],
[ 0., 0., 8., …, 16., 0., 0.],
…,
[ 0., 9., 16., …, 0., 0., 0.],
[ 0., 3., 13., …, 11., 5., 0.],
[ 0., 0., 0., …, 16., 9., 0.]],
…,
[[ 0., 0., 1., …, 1., 0., 0.],
[ 0., 0., 13., …, 2., 1., 0.],
[ 0., 0., 16., …, 16., 5., 0.],
…,
[ 0., 0., 16., …, 15., 0., 0.],
[ 0., 0., 15., …, 16., 0., 0.],
[ 0., 0., 2., …, 6., 0., 0.]],
[[ 0., 0., 2., …, 0., 0., 0.],
[ 0., 0., 14., …, 15., 1., 0.],
[ 0., 4., 16., …, 16., 7., 0.],
…,
[ 0., 0., 0., …, 16., 2., 0.],
[ 0., 0., 4., …, 16., 2., 0.],
[ 0., 0., 5., …, 12., 0., 0.]],
[[ 0., 0., 10., …, 1., 0., 0.],
[ 0., 2., 16., …, 1., 0., 0.],
[ 0., 0., 15., …, 15., 0., 0.],
…,
[ 0., 4., 16., …, 16., 6., 0.],
[ 0., 8., 16., …, 16., 8., 0.],
[ 0., 1., 8., …, 12., 1., 0.]]]), ‘DESCR’: “.. _digits_dataset:\n\
nOptical recognition of handwritten digits dataset\n
————————————————–\n\n**Data Set Characteristic
s:**\n\n :Number of Instances: 5620\n :Number of Attributes: 64\n :Attr
ibute Information: 8×8 image of integer pixels in the range 0..16.\n :Missing
Attribute Values: None\n :Creator: E. Alpaydin (alpaydin ‘@’ boun.edu.tr)\n
:Date: July; 1998\n\nThis is a copy of the test set of the UCI ML hand-written d
igits datasets\nhttp://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Ha
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment 5.html
3 of 5 3/11/2020, 6:57 PM
In [27]: import matplotlib.pyplot as plt
digits.images[0].shape
list = [10,100,100,45]
fig = plt.figure()
for i,j in enumerate(list):
plt.subplot(2,2,i+1)
plt.imshow(digits.images[j],cmap=’gray’)
In [2]: X = digits.data
y = digits.target
Step 1. Split the dataset into training data and testing data ( 10
points )
In [ ]:
Step 2. Algorithm Analysis ( 80 points )
Method 1. KNN
In [ ]:
Method 2. Linear SVM
In [ ]:
Method 3. Gaussian Kernal SVM
In [ ]:
Method 4. Naive Bayes
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment 5.html
4 of 5 3/11/2020, 6:57 PM
In [ ]:
Method 5. Decision Tree
In [ ]:
Method 6. Random Forest
In [ ]:
Method 7. Voting Classifier
In [ ]:
Method 8. Bagging
In [ ]:
Step 3: Accuracy Results Table ( 8 points )
KNN L_SVM RBF_SVM NB DT RF Voting Bagging
Accuracy
Weighted Precision
Weighted Recall
Step 4: Conclusion ( 2 Points )
In [ ]:
In [ ]:
In [ ]:
In [ ]:
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment 5.html
5 of 5 3/11/2020, 6:57 PM
{
“cells”: [
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“
“
Assignment 5: Multi-Classification
\n”,
“
\n”,
“\n”,
“
Due date: Mar 13th, 2020 (Friday)
\n”,
“
Total Points: 100
\n”,
“\n”,
“\n”,
“
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### Please put your name, student ID, date and time here \n”,
“* Name:\n”,
“* Student ID:\n”,
“* Date:\n”,
“* Time:”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“* In this assignment, you will investigate the handwritten digits dataset.\n”,
“* Sample images:\n”,
“
“\n”,
“* Please apply the folowing eight methods to classify the handwritten digits dataset.\n”,
“* Split the dataset into training sets and test sets\n”,
“* Fit the training data sets to the following eight algorithms\n”,
“* Print the classification report on the test data sets\n”,
“\n”,
“
Method 1: KNN
\n”,
“
Method 2: Linear SVM
\n”,
“
Method 3: Gaussian Kernel SVM
\n”,
“
Method 4: Naive Bayes
\n”,
“
Method 5: Decision Tree
\n”,
“
Method 6: Random Forest
\n”,
“
Method 7: Voting Classifier
\n”,
“
Method 8: Bagging
”
]
},
{
“cell_type”: “code”,
“execution_count”: 4,
“metadata”: {
“scrolled”: true
},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“{‘data’: array([[ 0., 0., 5., …, 0., 0., 0.],\n”,
” [ 0., 0., 0., …, 10., 0., 0.],\n”,
” [ 0., 0., 0., …, 16., 9., 0.],\n”,
” …,\n”,
” [ 0., 0., 1., …, 6., 0., 0.],\n”,
” [ 0., 0., 2., …, 12., 0., 0.],\n”,
” [ 0., 0., 10., …, 12., 1., 0.]]), ‘target’: array([0, 1, 2, …, 8, 9, 8]), ‘target_names’: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), ‘images’: array([[[ 0., 0., 5., …, 1., 0., 0.],\n”,
” [ 0., 0., 13., …, 15., 5., 0.],\n”,
” [ 0., 3., 15., …, 11., 8., 0.],\n”,
” …,\n”,
” [ 0., 4., 11., …, 12., 7., 0.],\n”,
” [ 0., 2., 14., …, 12., 0., 0.],\n”,
” [ 0., 0., 6., …, 0., 0., 0.]],\n”,
“\n”,
” [[ 0., 0., 0., …, 5., 0., 0.],\n”,
” [ 0., 0., 0., …, 9., 0., 0.],\n”,
” [ 0., 0., 3., …, 6., 0., 0.],\n”,
” …,\n”,
” [ 0., 0., 1., …, 6., 0., 0.],\n”,
” [ 0., 0., 1., …, 6., 0., 0.],\n”,
” [ 0., 0., 0., …, 10., 0., 0.]],\n”,
“\n”,
” [[ 0., 0., 0., …, 12., 0., 0.],\n”,
” [ 0., 0., 3., …, 14., 0., 0.],\n”,
” [ 0., 0., 8., …, 16., 0., 0.],\n”,
” …,\n”,
” [ 0., 9., 16., …, 0., 0., 0.],\n”,
” [ 0., 3., 13., …, 11., 5., 0.],\n”,
” [ 0., 0., 0., …, 16., 9., 0.]],\n”,
“\n”,
” …,\n”,
“\n”,
” [[ 0., 0., 1., …, 1., 0., 0.],\n”,
” [ 0., 0., 13., …, 2., 1., 0.],\n”,
” [ 0., 0., 16., …, 16., 5., 0.],\n”,
” …,\n”,
” [ 0., 0., 16., …, 15., 0., 0.],\n”,
” [ 0., 0., 15., …, 16., 0., 0.],\n”,
” [ 0., 0., 2., …, 6., 0., 0.]],\n”,
“\n”,
” [[ 0., 0., 2., …, 0., 0., 0.],\n”,
” [ 0., 0., 14., …, 15., 1., 0.],\n”,
” [ 0., 4., 16., …, 16., 7., 0.],\n”,
” …,\n”,
” [ 0., 0., 0., …, 16., 2., 0.],\n”,
” [ 0., 0., 4., …, 16., 2., 0.],\n”,
” [ 0., 0., 5., …, 12., 0., 0.]],\n”,
“\n”,
” [[ 0., 0., 10., …, 1., 0., 0.],\n”,
” [ 0., 2., 16., …, 1., 0., 0.],\n”,
” [ 0., 0., 15., …, 15., 0., 0.],\n”,
” …,\n”,
” [ 0., 4., 16., …, 16., 6., 0.],\n”,
” [ 0., 8., 16., …, 16., 8., 0.],\n”,
” [ 0., 1., 8., …, 12., 1., 0.]]]), ‘DESCR’: \”.. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n————————————————–\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 5620\\n :Number of Attributes: 64\\n :Attribute Information: 8×8 image of integer pixels in the range 0..16.\\n :Missing Attribute Values: None\\n :Creator: E. Alpaydin (alpaydin ‘@’ boun.edu.tr)\\n :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttp://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32×32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8×8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n – C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n Graduate Studies in Science and Engineering, Bogazici University.\\n – E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n – Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n Linear dimensionalityreduction using relevance weighted LDA. School of\\n Electrical and Electronic Engineering Nanyang Technological University.\\n 2005.\\n – Claudio Gentile. A New Approximate Maximal Margin Classification\\n Algorithm. NIPS. 2000.\”}\n”
]
}
],
“source”: [
“# Importing the dataset\n”,
“from sklearn.datasets import load_digits\n”,
“digits = load_digits()\n”,
“print(digits)”
]
},
{
“cell_type”: “code”,
“execution_count”: 27,
“metadata”: {},
“outputs”: [
{
“data”: {
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“
]
},
“metadata”: {
“needs_background”: “light”
},
“output_type”: “display_data”
}
],
“source”: [
“import matplotlib.pyplot as plt\n”,
“digits.images[0].shape\n”,
“list = [10,100,100,45]\n”,
“fig = plt.figure()\n”,
“for i,j in enumerate(list):\n”,
” plt.subplot(2,2,i+1)\n”,
” plt.imshow(digits.images[j],cmap=’gray’)”
]
},
{
“cell_type”: “code”,
“execution_count”: 2,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: [
“X = digits.data\n”,
“y = digits.target”
]
},
{
“cell_type”: “markdown”,
“metadata”: {
“collapsed”: true
},
“source”: [
“## Step 1. Split the dataset into training data and testing data (10 points)”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Step 2. Algorithm Analysis (80 points)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 1. KNN”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 2. Linear SVM”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 3. Gaussian Kernal SVM”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 4. Naive Bayes”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 5. Decision Tree”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Method 6. Random Forest”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {
“collapsed”: true
},
“source”: [
“## Method 7. Voting Classifier”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {
“collapsed”: true
},
“source”: [
“## Method 8. Bagging”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “markdown”,
“metadata”: {
“collapsed”: true
},
“source”: [
“## Step 3: Accuracy Results Table (8 points)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {
“collapsed”: true
},
“source”: [
“
| KNN | L_SVM | RBF_SVM | NB | DT | RF | Voting | Bagging | |
|---|---|---|---|---|---|---|---|---|
| Accuracy | ||||||||
| Weighted Precision | ||||||||
| Weighted Recall |
”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“## Step 4: Conclusion (2 Points)”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: []
}
],
“metadata”: {
“kernelspec”: {
“display_name”: “Python 3”,
“language”: “python”,
“name”: “python3”
},
“language_info”: {
“codemirror_mode”: {
“name”: “ipython”,
“version”: 3
},
“file_extension”: “.py”,
“mimetype”: “text/x-python”,
“name”: “python”,
“nbconvert_exporter”: “python”,
“pygments_lexer”: “ipython3”,
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