Plain accuracy metric calculated based on the actual classes vs predicted classes using a default class division threshold.
Calculates the Area Under Curve for given labels and predictions.
Calculates Confusion Metric on Binary Classification or Multivariate Classification labels.
F1 score is calculated by using the precision and recall on the label and predictions.
GINI metric is two times the ROC AUC score minus 1. This metric gives a good measure of the model’s performance when the target classes are not balanced.