Hands-On Artificial Intelligence for Cybersecurity

F1 value

For the convenience, let's briefly go over the metrics that were previously introduced and their definitions:

Sensitivity or True Positive Rate (TPR) = True Positive / (True Positive + False Negative);

Here, sensitivity is also known as the recall rate:

False Positive Rate (FPR) = False Positive / (False Positive + True Negative); Precision = True Positive / (True Positive + False Positive)

On the basis of these metrics, it is possible to estimate the F1 score, which represents the harmonic average between precision and sensitivity:

F1 = 2 * Precision * Sensitivity / (Precision + Sensitivity)

The F1 score can be used to evaluate the results that were obtained from the predictions; the best estimates are obtained with F1 values close ...