Table 3.

Performance measures for the binary classification problem: TP – true positives, TN – true negatives, FP – false positives, FN – false negatives

Measure	Mathematical formulation	Comment
Accuracy	A $=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}$	Indicates the fraction of correct predictions over the total: not very significant when dealing with imbalanced data.
Precision	P $=\frac{\text{TP}}{\text{TP}+\text{FP}}$	Indicates the fraction of relevant instances among the retrieved ones.
Recall	R $=\frac{\text{TP}}{\text{TP}+\text{FN}}$	Indicates the fraction of relevant instances that have been retrieved over the total relevant instances.
F1 score	F${}_1=2\times \frac{\mathrm{P}\times \mathrm{R}}{\mathrm{P}+\mathrm{R}}$	It is the harmonic mean of precision and recall.
Matthews correlation coefficient	MCC $=\frac{\text{TP}\times \text{TN}-\text{FP}\times \text{FN}}{\sqrt{(\text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+\text{FN})}}$	Returns a value between −1 and +1: +1 represents a perfect prediction, 0 no better than random prediction and −1 indicates total disagreement between prediction and observation.