Tutorial by Examples

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means that the top left corner of the plot is the “ideal” point - a false positive rate of zero...

One needs the predicted probabilities in order to calculate the ROC-AUC (area under the curve) score. The cross_val_predict uses the predict methods of classifiers. In order to be able to get the ROC-AUC score, one can simply subclass the classifier, overriding the predict method, so that it would a...