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Glossary term
A measure of a classifier's ability to separate classes across all decision thresholds.
A classifier outputs scores, and the threshold you pick changes its trade-off. The ROC curve plots the true-positive rate against the false-positive rate at every threshold. AUC is the area under that curve: 1.0 is perfect and 0.5 is no better than guessing.
It measures how well the model ranks positives above negatives, regardless of any one threshold.
AUC with scikit-learn
from sklearn.metrics import roc_auc_score
print(roc_auc_score(y_true, y_scores))It is a standard summary of a classifier's quality that does not depend on choosing a threshold.