Consider the following approach for testing whether a classifier A beats another classifier B. Let N be the size of a given data set, pA be the accuracy of classifier A, pB be the accuracy of classifier B, and p = (pA + pB)/2 be the average accuracy for both classifiers. To test whether classifier A is significantly better than B, the following Z-statistic is used:

Classifier A is assumed to be better than classifier B if Z > 1.96

Table 4.9 compares the accuracies of three different classifiers, decision tree classifiers, nave Bayes classifiers, and support vector machines, on various data sets. (The latter two classifiers are described in Chapter 5.)

Summarize the performance of the classifiers given in Table 4.9 using the following 3 3 table:

Each cell in the table contains the number of wins, losses, and draws when comparing the classifier in a given row to the classifier in a given column.

A step-by-step would be very helpful to help understand the type of problem.

Z=N2p(1p)pApB Tahle 4 a Comnarinn the accuraru of varinue rlaccifiration mathnole \begin{tabular}{|l|c|c|c|} \hline win-loss-draw & Decision tree & Nave Bayes & Supportvectormachine \\ \hline Decision tree & 0023 & & \\ \hline Nave Bayes & & 0023 & \\ \hline Support vector machine & & & 0023 \\ \hline \end{tabular} Z=N2p(1p)pApB Tahle 4 a Comnarinn the accuraru of varinue rlaccifiration mathnole \begin{tabular}{|l|c|c|c|} \hline win-loss-draw & Decision tree & Nave Bayes & Supportvectormachine \\ \hline Decision tree & 0023 & & \\ \hline Nave Bayes & & 0023 & \\ \hline Support vector machine & & & 0023 \\ \hline \end{tabular}