AUC
Area Under the ROC Curve — a threshold-independent measure of classifier ranking quality.
AUC (Area Under the ROC Curve) measures a binary classifier’s ability to rank positive examples above negative ones across all decision thresholds, giving a single score from 0 to 1. Because it is threshold-independent, it reflects overall discrimination rather than performance at one cutoff, making it more informative than accuracy when classes are imbalanced. The key exam distinction: a model can score high accuracy by always predicting the majority class yet still have low AUC. Amazon SageMaker’s XGBoost and linear learner report AUC as an objective metric, and Automatic Model Tuning can optimize for it.
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