[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-precision-and-recall::en":3,"gloss-cluster-precision-and-recall::en":26,"gloss-next-precision-and-recall::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"precision-and-recall","mlops","Precision and Recall","Precision and recall are the two halves of a classifier's or retriever's quality, and they answer different questions. Precision asks: of the items the system flagged, what share were actually correct? Recall asks: of the items that should have been flagged, what share did the system find? A spam filter with high precision rarely sends good mail to the junk folder but may let spam through; one with high recall catches nearly all spam but occasionally quarantines a real invoice. Accuracy — the share of all predictions that were right — hides this trade-off and becomes actively misleading when one class is rare, since a model that never flags anything can be 99% accurate on a 1% problem while finding nothing. The trade-off is usually a threshold, not a fixed property. Lowering the score at which the system acts raises recall and lowers precision; raising it does the reverse. Which side to favour is a product decision, not a modelling one: it depends on what a false positive costs relative to a false negative in your workflow. Where a single number is needed, the F1 score combines the two as a harmonic mean, but reporting F1 alone hides which side of the trade-off a change moved. In retrieval-augmented systems the same pair applies to the retrieval step, where recall usually matters more up front — a passage that is never retrieved cannot be reranked — and precision is recovered later by reranking.","Precision and recall answer different questions about a classifier or retriever, and accuracy hides the trade-off between them whenever one class is rare.",null,[11,14,17,20,23],{"slug":12,"name":13},"class-imbalance","Class Imbalance",{"slug":15,"name":16},"eval-harness","Eval Harness",{"slug":18,"name":19},"golden-dataset","Golden Dataset",{"slug":21,"name":22},"reranking","Reranking",{"slug":24,"name":25},"retrieval-evaluation","Retrieval Evaluation",[27,31,35,38,41,45,46,49,52,55,58,59],{"slug":28,"category":5,"name":29,"updated_at":30},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":36,"category":5,"name":37,"updated_at":34},"batch-inference","Batch Inference",{"slug":39,"category":5,"name":40,"updated_at":34},"canary-prompt","Canary Prompt",{"slug":42,"category":5,"name":43,"updated_at":44},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":12,"category":5,"name":13,"updated_at":34},{"slug":47,"category":5,"name":48,"updated_at":34},"continuous-batching","Continuous Batching",{"slug":50,"category":5,"name":51,"updated_at":34},"cross-validation","Cross-Validation",{"slug":53,"category":5,"name":54,"updated_at":34},"data-labeling","Data Labeling",{"slug":56,"category":5,"name":57,"updated_at":44},"drift-detection","Drift Detection",{"slug":15,"category":5,"name":16,"updated_at":44},{"slug":60,"category":5,"name":61,"updated_at":44},"experiment-tracking","Experiment Tracking"]