Table 3 Evaluation metrics

\(Accuracy = \frac{{{\text{correct}}\;{\text{predictions}}}}{{{\text{total}}\;{\text{predictions}}}}\)

Useful when target classes are well balanced

\(Recall = \frac{True\;positives}{{{\text{True}}\;{\text{positives}} + {\text{False}}\;{\text{negatives}}}}\)

The ability of a model to find all relevant cases within a dataset

\(Precision = \frac{True\;positives}{{{\text{True}}\;{\text{positives}} + {\text{False}}\;{\text{positives}}}}\)

The ability of a model to identify only the relevant data points

\(F1 - score = \frac{{2\left( {precision \times recall} \right)}}{precision + recall}\)

Combination between Precision and Recall

Used to punish extreme values