Measuring the Performance of a Classifier

This program is provided to support the description of the calculation of performance measures given in Chapter 12 of Principles of Data Mining by Max Bramer.

Given a table (such as Figure 12.1 on Page 176) containing the number of true and false positives and negatives produced by a classifier, it calculates the performance measures shown in Figure 12.2 (Page 179).

Enter the values of TP, FP, FN and TN and click on 'Submit'

		Predicted Class
		Positive	Negative
Actual Class	Positive	TP	FN
Actual Class	Negative	FP	TN

TP - number of true positives: instances classified as positive that are actually positive
FP - number of false positives: instances classified as positive that are actually negative ('type 1' errors)
FN - number of false negatives: instances classified as negative that are actually positive ('type 2' errors)
TN - number of true negatives: instances classified as negative that are actually negative