• AUC score is the area under the ROC Curve
• AUC score is used to compare multiple classifiers
• Greater the AUC score, better the model
• AUC more than 0.5 is better than random classifier
• AUC score is robust to class imbalance
• AUC Score is calculated from True Positive Rate and False Positive Rate
  • For different threshold, the TPR and FPR is plotted on the graph
  • All the point will be connected by a line
  • And the score is the Area Under the Curve of that line
• Range = $[0,1]$
• If you need to find best threshold for one model, then ROC Curve, Area Under Precision Recall Curve (AUPRC)

AUC score - area under the curve

More AUC is better