Table 3.

Evaluation metrics

$Accuracy=\frac{\text{correct}\text{predictions}}{\text{total}\text{predictions}}$	Useful when target classes are well balanced
$Recall=\frac{Truepositives}{\text{True}\text{positives}+\text{False}\text{negatives}}$	The ability of a model to find all relevant cases within a dataset
$Precision=\frac{Truepositives}{\text{True}\text{positives}+\text{False}\text{positives}}$	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