ROC (Receiver Operating Characteristic) curve and AUC (Area Under the Curve) are important evaluation metrics used in machine learning classification problems:
ROC curve is a probability curve that plots the True Positive Rate (Sensitivity) vs False Positive Rate at various classification thresholds.
It provides an indication of model performance independent of class distribution or error costs.
AUC represents the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1).
It measures how well a model can distinguish between classes – an AUC of 0.5 means the model performs no better than random chance, while 1.0 is perfect classification.
AUC has a clear probabilistic interpretation and is a discriminative measure – provides information on how well models can discriminate between classes.
Unlike accuracy, it is not influenced by class imbalance or error distribution.
Commonly used for binary and multi-class classification problems to compare models and select the best performing one.
Higher AUC indicates better discriminative ability even at various thresholds and more robust model not dependant on single classification threshold.
So in summary, ROC-AUC is a powerful evaluation metric to assess and compare classification models independent of various data characteristics or thresholds.
