Hands-On Neuroevolution with Python

ROC AUC

We create the ROC by plotting true positive rates against false positive rates at different thresholds. It shows the performance of the classification model at different thresholds.

The true positive rate (TPR) is a synonym of recall, which we discussed earlier. It can be given by this formula:

The false positive rate is calculated as follows:

TN is the true negatives.

The AUC allows us to estimate the discrimination power of the classification model, that is, the ability of the model to correctly rank the random positive points more ...