Glossary · Phase 3 · Machine learning
False positive rate
The share of real negatives the model wrongly flags, such as the share of loyal customers it says will churn.
Learn it properly
ROC curves and AUC · Judge a classifier across every threshold. About 30 minutes.
Other terms from the same lesson
- AUC: Area under the ROC curve: the chance the model scores a random true case above a random false one. 1 is perfect, 0.5 is guessing.
- average precision: The area under the precision-recall curve: one number for how well a model finds a rare class.
- calibration: Whether a model's probabilities can be taken at face value: of the customers it gives 30%, about 30% really churn.
- precision-recall curve: A chart of precision against recall as the threshold moves. More telling than the ROC curve when the thing you're looking for is rare.
- ROC curve: A chart of how a classifier trades catching more true cases against raising more false alarms.
- true positive rate: The share of real positives the model catches, such as the share of churners it flags. Another name for recall.