hesela.devClass prevalence
Class prevalence is the proportion of observations belonging to a specified class in a defined population or evaluation sample.
For a nonempty binary sample, positive prevalence = (TP + FN) / (TP + FP + FN + TN). Predicting every observation positive gives precision equal to that prevalence. [1] Sampling more failures changes the evaluation prevalence and may change precision; a balanced test sample does not directly establish deployment alert quality. [2] For storage, specify whether an observation is a drive or a drive-day window, and whether positive means failure within a stated horizon; this proportion is not AFR. [3]
Relations
- related
- precision, recall, false-positive-rate, average-precision, drive-day, afr
Citations
- scikit-learn: precision_recall_curve; the all-positive endpoint.
- Saito and Rehmsmeier. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE 10(3): e0118432 (2015).
- Hesela: disk failure prediction, metric denominators and a synthetic alert-count example.