Data Mining

Consider the following approach for testing whether a classifier A beats another classifier B. Let N be the size of a given dataset, 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:

Z=(pApB) / (2p(1p)) / N)

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

Table 3.8 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 4.)

Summarize the performance of the classifiers given in Table 3.8 using the following 33 table:

win-loss-draw	Decision tree	Nave Bayes	Support vector machine
Decision tree	0 - 0 - 23
Nave Bayes		0 - 0 - 23
Support vector machine			0 - 0 - 23

Table 3.8:

Data Set	Size	Decision	nave	Support vector
	(N)	Tree (%)	Bayes (%)	machine (%)
Anneal	898	92.09	79.62	87.19
Australia	690	85.51	76.81	84.78
Auto	205	81.95	58.05	70.73
Breast	699	95.14	95.99	96.42
Cleve	303	76.24	83.5	84.49
Credit	690	85.8	77.54	85.07
Diabetes	768	72.4	75.91	76.82
German	1000	70.9	74.7	74.4
Glass	214	67.29	48.59	59.81
Heart	270	80	84.07	83.7
Hepatitis	155	81.94	83.23	87.1
Horse	368	85.33	78.8	82.61
Ionosphere	351	89.17	82.34	88.89
Iris	150	94.67	95.33	96
Labor	57	78.95	94.74	92.98
Led7	3200	73.34	73.16	73.56
Lymphography	148	77.03	83.11	86.49
Pima	768	74.35	76.04	76.95
Sonar	208	78.85	69.71	76.92
Tic-tac-toe	958	83.72	70.04	98.33
Vehicle	846	71.04	45.04	74.94
Wine	178	94.38	96.63	98.88
Zoo	101	93.07	93.07	96.04

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.

Please be thorough in explanation/work to get to answer