- 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 =
- If you need to find best threshold for one model, then ROC Curve, Area Under Precision Recall Curve (AUPRC)
