Question

Suppose we want to compare the performance of two algorithms (Naive Bayes & Support
Vector Machine) on 10 different data sets. The table below gives the performance measures
and we would like to perform Wilcoxon’s Signed-Rank Test with null hypothesis which states
that the two algorithms do not behave differently from one another.
NB 0.9643 0.7342 0.7230 0.7170 0.7167 0.7436 0.7063 0.8321 0.9822 0.6962
SVM 0.9944 0.8134 0.9151 0.6616 0.7167 0.7708 0.6221 0.8063 0.9358 0.9990
Now consider the following statements.
I. Test value is 17.
II. Test value is 28.
III. We fail to reject the null hypothesis at the 0.05 level of significance.
Which of the above statement(s) is/are correct?
(A) Only I.
(B) Only I and III.
(C) Only II and III.
(D) Only III.
(E) None of the above.

Answer 1

Answer:

This article discusses the theory behind the Naive Bayes classifiers and their implementation.

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

To start with, let us consider a dataset.

Consider a fictional dataset that describes the weather conditions for playing a game of golf. Given the weather conditions, each tuple classifies the conditions as fit(“Yes”) or unfit(“No”) for plaing golf.

Here is a tabular representation of our dataset.