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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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AUC PR optimizes results for inferences for the less common class https://docs.cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/train-model
AUC-ROC (Area Under the Receiver Operating Characteristic) and AUC-PR (Area Under the Precision-Recall Curve) are both metrics for evaluating binary classifiers, but AUC-ROC measures the trade-off between true positive rate and false positive rate, making it less sensitive to class imbalance and more appropriate for balanced class distribution, while AUC-PR focuses on the minority positive class by evaluating precision and recall, making it more appropriate for imbalanced datasets where correctly identifying positives is critical, such as in fraud detection or spam detection.
o AUC-PR focuses on how well the classifier performs for the positive class (precision and recall are both concerned with positives) --> more suitable when the focus is on indentifying the positive class in imbalanced data o AUC-ROC looks at the trade-off between the true positive rate (sensitivity) and the false positive rate, considering both classes. --> general purpose meric that works well when both classes are of similiar size ( ChatGPT )