Table 3.

Template of a confusion matrix for a 3-class classifier.

	Definition	Formula
True Positive (TP)	The number of samples of a class which have been correctly classified	$T{P}_i=n_{ii}$
True Negative (TN)	The number of samples of other classes which has been correctly classified	$T{N}_i={\displaystyle {\sum }_{j\ne i}{\displaystyle {\sum }_{k\ne i}{n}_{jk}}}$
False Positive (FP)	The number of samples not belongs to a class which has been incorrectly classified as belonging to it	$F{P}_i={\displaystyle {\sum }_{k\ne i}{n}_{ki}}$
False Negative (FN)	The number of samples belonging to a class which have been incorrectly classified as belong to other class	$F{N}_i={\displaystyle {\sum }_{k\ne i}{n}_{ik}}$
Accuracy	The proportion of all samples which have been correctly classified	$Acc=\frac{{\displaystyle {\sum }_i{n}_{ii}}}{T{P}_i+T{N}_i+F{P}_i+F{N}_i}$
Sensitivity	The proportion of samples which have been correctly classified	$Sen{s}_i=\frac{T{P}_i}{T{P}_i+F{P}_i}$
Precision	The proportion of sample predicted to belong to a class which is correct	$Pre{c}_i=\frac{T{P}_i}{T{P}_i+F{P}_i}$
Specificity	The proportion of negative samples which have been correctly classified to be negative	$Spe{c}_i=\frac{T{N}_i}{F{P}_i+T{N}_i}$
F-Measure	the weighted average of the precision and sensitivity	$F1=\frac{2}{1/Sen{s}_i+1/Pre{c}_i}$