Glossary · Phase 3 · Machine learning
Recall
Of the things that should have been flagged, the share the model caught.
Learn it properly
Precision, recall and F1 · Accuracy lies; these don't. About 30 minutes.
Other terms from the same lesson
- F1 score: One number that balances precision and recall.
- false negative: A no the model got wrong: it predicted the customer would stay, but they churned.
- false positive: A yes the model got wrong: it predicted churn, but the customer stayed.
- harmonic mean: An average that stays low if either number is low. F1 is the harmonic mean of precision and recall.
- macro average: Averaging a score across classes with each class counted equally, however rare it is.
- precision: Of the things a model flagged, the share that were right.
- true negative: A no the model got right: it predicted the customer would stay, and they did.
- true positive: A yes the model got right: it predicted churn, and the customer did churn.