• 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 (Sensitivity) and False Positive Rate (1-Specificity)
  • 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 = $[0,1]$
• This score from multiple model can be used to find out the best model, i.e., the more AUC score means the model is more correct or better.
• Another alternative is Area Under Precision Recall Curve (AUPRC)
• If you need to find best threshold for one model, then ROC Curve, Precision Recall Curve (PRC)

AUC score - area under the curve

More AUC is better