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Precision Recall curve vs Area under curve vs ROC curve

 

MetricPrecision-Recall CurveArea Under Curve (AUC)ROC Curve
PurposeEvaluates a binary classifier's ability to balance precision and recall.Measures the ability of a classifier to distinguish between positive and negative classes.Evaluates a classifier's trade-off between true positive rate and false positive rate.
FocusFocuses on the performance of the positive class (relevant instances).Focuses on the overall model performance regardless of class distribution.Focuses on the ability to classify positive and negative instances.
Threshold SelectionHelps identify an optimal threshold for classification based on desired precision or recall.Doesn't directly suggest an optimal threshold.Helps identify an optimal threshold for classification based on the trade-off between true positive and false positive rates.
Imbalanced ClassesParticularly useful for imbalanced datasets where the negative class dominates.Sensitive to class imbalance, might not perform well with imbalanced data.Less sensitive to class imbalance, can still perform well with imbalanced data.
InterpretabilityProvides insights into the classifier's ability to correctly identify relevant instances.Less interpretable in terms of precision and recall.Less interpretable in terms of true positive and false positive rates.
Trade-offAllows for adjusting the trade-off between precision and recall by selecting a threshold.Measures the overall discriminatory power of the classifier across different thresholds.Measures the classifier's ability to distinguish between classes at different thresholds.
Example Use CaseMedical diagnosis where false negatives are costly (cancer detection).Credit scoring where overall predictive accuracy is essential.Spam email detection where reducing false positives is critical.
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