# Metric

# Accuracy

Plain accuracy metric calculated based on the actual classes vs predicted classes using a default class division threshold.

# AUC Score

Calculates the Area Under Curve for given labels and predictions.

# Confusion Matrix

Calculates Confusion Metric on Binary Classification or Multivariate Classification labels.

# F1 Score

F1 score is calculated by using the precision and recall on the label and predictions.

# GINI

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.