How to evaluate a classifier?

The classifier can be evaluated by building the confusion matrix. Confusion matrix shows the total number of correct and wrong predictions. Confusion Matrix for class label positive(+VE) and negative(-VE)is shown below;

	Actual Class(Target)
	+VE	-VE
Predicted Class (Model)	+VE	A = True +VE	B = False -VE	+VE prediction	P=A / (A+B)
-VE	C = False +VE	D = True -VE	-VE prediction	D / (C + D)
	Sensitivity	Specificity	Accuracy = A + D / (A + B + C + D)
A / (A + C)	D / (B + D)

[quads id=1]

Accuracy:

Accuracy is the proportion of the total number of correct predictions. e.g Accuracy = A + D / (A + B + C + D)

Error-Rate:

Error Rate = 1 – Accuracy

+VE predictions:

+VE predictions are the proportion of the total number of correct positive predictions. +VE predictions = A / (A+B) [quads id=2]

-VE predictions:

-VE predictions are the proportion of the total number of correct negative predictions. -VE predictions = D / (C + D)

Precision:

Precision is the correctness that how much tuple are

+VE and classifier predicted them as +VE
-VE and classifier predicted them as -VE

Precision = A / P

Recall:

Recall = A / Real positive Sensitivity (Recall): Sensitive is the total True +VE rate. The correction of the actual positive cases that are correctly identified. Sensitivity (Recall) = A / (A + C) F-Measure:

F-Measure is harmonic mean of recall and precision.

F-Measure = 2 * Precision * Recall / Precision + Recall

Specificity:

Specificity is true -VE rate. Specificity is the proportion of the actual -VE cases that are correctly identified. Specificity = D / (B + D) Note: Specificity of one class is same as the sensitivity of the other class.

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Evaluation of a classifier by confusion matrix in data mining
By: Prof. Dr. Fazal Rehman | Last updated: March 3, 2022