Table 6 Evaluation metrics Precision...

Table 6

Evaluation metrics

Precision	Fraction of the correct decisions to the total number of the given decisions in a particular class	$\frac{TP}{TP + FP}$
Recall	Fraction of the correct decisions that are given by the machine learning method to the total number of cases in a particular subset	$\frac{TP}{TP + FN}$
Accuracy	Measures how close the obtained decisions are to the actual classification	$\frac{TP + TN}{TP + TN + FP + FN}$
F1-score	Harmonic mean of precision and recall	$\frac{2 * P r e c i s i o n * R e c a l l}{P r e c i s i o n + R e c a l l}$

Note(s): TP – True Positive, FP- False Positive, TN – True Negative and

FN – False Negative

Sharing Unavailable