hesela.devAverage precision
Non-interpolated average precision summarizes ranked prediction scores by weighting precision at each threshold by the corresponding increase in recall.
The scikit-learn convention is AP = sum((R_n - R_(n-1)) * P_n), with points ordered by increasing recall and initial recall zero. It uses score thresholds, not just one binary decision. Trapezoidal PR-AUC instead integrates linear segments between precision-recall points; it need not equal AP. State the algorithm, positive class and any averaging across classes. [1]
Relations
- related
- precision, recall, class-prevalence
Limitations
- AP does not specify an operating threshold or alert budget, and an evaluation sample without positive outcomes cannot establish failure-detection quality. [1]