Table 5.

(a) Confusion matrix; (b) Formula for evaluation metrics.

(a) Confusion Matrix, contains 4 different values (FP, TP, FN, TN) for evaluating the performance of classification models when the predictive scores are binary (represented as zeros and ones).
		Actual
		Positive (1)	Negative (0)
Predicted	Positive (1)	TP	TN
	Negative (0)	FP	FN
(b) Evaluation metric to assess the performance of the models using accuracy, precision (positive predictive value—PPV), recall (sensitivity), and F-Score (a harmonic mean of sensitivity).
Evaluation Metric		Formulation
Accuracy		$\frac{\mathrm{TP} + \mathrm{TN}}{\mathrm{N}}$
PPV		$\frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}$
Recall		$\frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN} }$
F-Score		$\frac{2 \ast \mathrm{TP}}{2 \ast \mathrm{TP} + \mathrm{FP} + \mathrm{FN} }$

Abbreviations: TP, True Positive; TN, True Negative; N, total number of genes in the database; FP, False Positive; FN, False Negative; N, total number of samples.