hesela.devFalse positive rate
The false positive rate is the fraction of observed negative cases incorrectly classified as positive at a specified decision threshold.
FPR = FP / (FP + TN), with FP false positives and TN true negatives, so FPR equals one minus specificity. It is undefined without observed negatives. The fraction of alerts that are false is instead FP / (TP + FP), or one minus precision when alerts exist. [1] State the denominator whenever using the ambiguous phrase false alarm rate. Low FPR need not imply high precision when failures are rare. [2]
Relations
- related
- precision, recall, class-prevalence, smart
Citations
- scikit-learn: model evaluation, confusion-matrix rates.
- 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.