Performance Curve: More Intuitive than ROC/PRC and Less Assumptive than Threshold Metrics

An evaluation method for binary classifiers that combines the best of both worlds

There are two most common families of evaluation metrics used in the context of binary classification:

• Threshold metrics (e.g.: accuracy, precision, recall, F-measure)
• Ranking metrics (e.g. receiver operating characteristics (ROC) curve, precision-recall (PR) curve)

Problems with threshold metrics

Although threshold metrics are intuitive and easy to be explained to non-technical audience, they require a specific threshold cutoff to separate between a …

data science editors pick evaluation metrics performance roc