Table 1.

Confusion Matrix Components.

Confusion Matrix	Definition	Formula
Accuracy	It is the ratio of correctly classified to the whole set. For instance, which answers the question: How many patients did we correctly diagnosed as depressed out of all the patients?	TN + TP/All
Precision	It is the ratio of correctly classified positive subjects to all the positive subjects. For instance, which answers the question: How many of the patients whom we named as depressed are actually depressed?	TP/TP + FP
Sensitivity (Recall)	It is the ratio of correctly classified positive subjects to all those who have the disease in reality. Which answers the question: Of all the depressed people in the dataset, how many did we correctly predict as depressed?	TP/TP + FN
Specificity	It is the ratio of correctly classified negative subjects to all the healthy subjects in reality. Which answers the question: Of all the healthy people in the dataset, how many we correctly predict as not depressed?	TN/TN + FP
FMeasure	It is a combination of both recall and precision. Harmonic average.	2 × (Precision × Recall)/(Recall + Precision)