Data Mining Assignment
Provide a real world example of a probability distribution based on your readings for the week. Consider the data instances that is different from uniform (i.e., equal probability).
Instructions for participation: You must post an INITIAL POST and then respond to at least ONE POST. In both postings, you MUST provide citable academic literature to defend your use case. Please do not use the same source over again, especially when most times there is absolutely no applicability from one post to the next.
Satheesh Kumar Thatiparti
Week 2 Discussion
COLLAPSE
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The sample with the highest probability is then selected for distribution to increase the probability of that sample. An example of how a Probability Distribution is built in the case study. In this case study, each row represents the number of records from the training data, and the second and subsequent rows represent the number of records in the test data. For a probability distribution to be statistically significant, it must have a distribution that has a majority of 1s (Kornaropoulos et al., 2020). If two random samples have the same proportion of 1s, then the distribution should have a majority of 1s. For a probability distribution to be truly random, it must be unpredictable. If all the samples have the same proportion of 1s, the distribution should be completely random. If the samples have a different proportion of 1s, then the distribution must be unpredictable. If the samples differ in length, then the distribution must be completely unpredictable. The following example shows how to deal with such a problem. This is very basic, and there are probably many such cases of the preceding type that could be applied. This is a relatively simple example, and it shows us a simple situation in which a binary stream is being used (Kornaropoulos et al., 2020).
Data set represents the data set that contains examples of the observed distribution. For example, if the distribution represents a binomial distribution, data set, data set will be binomial. If the data set contains data from a representative sample, and each data set member has a data attribute, then the data set distribution is representative. Suppose the distribution represents a standard distribution with an unknown sample and the number of observations is too small. In that case, a decision tree can be built for the data only, but the results cannot be useful. For data sets with n samples, the decision tree could be constructed using any node with sample nodes. That is, each node has at most two classes. To construct such a model, need a simple method for creating a decision tree from extensive data (Arun Srivatsan et al., 2018).
Reference
Arun Srivatsan, R., Xu, M., Zevallos, N., & Choset, H. (2018). Probabilistic pose estimation using a bingham distribution-based linear filter. The International Journal of Robotics Research, 37(13-14), 1610-1631.
Kornaropoulos, E. M., Papamanthou, C., & Tamassia, R. (2020, May). The state of the uniform: attacks on encrypted databases beyond the uniform query distribution. In 2020 IEEE Symposium on Security and Privacy (SP) (pp. 1223-1240). IEEE.
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