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Evaluation & TrainingPrecision/recallF1 score

Precision and recall

The two error-facing rates of a classifier: how often a yes is right, and how often a real yes is caught, plus the F1 that blends them.

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Evaluation & Training
Also known as
Precision/recall, F1 score
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docs.typesafe.ai
Added
2026-10-12

Definition

Precision is the share of predicted positives that are truly positive; recall is the share of true positives the model found. They trade off against the threshold, and which one matters depends on the cost of a false positive versus a false negative.

Because Jev returns a probability, the pair is not a fixed property of the model — raising the threshold generally lifts precision and lowers recall. Report both, and the threshold they were measured at, or the number is not reproducible.

Tagsevaluationbenchmarks

Related terms

Definitions that connect to this one.

Confusion matrixThe table of predicted versus actual labels that shows which classes a model actually confuses, not just how often it is right.Evaluation & TrainingBase rateHow common the positive class is in your traffic, which sets the starting probability a decision model has to move away from.Answers & ConfidenceBenchmark claimsTypeSafe's headline multipliers — 193.6x faster, 444.6x cheaper, a 238x lower input price — come from its own workflow comparisons.Evaluation & TrainingCalibrationThe property that outcomes given a probability of 0.2 occur about 20% of the time, measured across many predictions.Answers & ConfidenceOracle floorThe best error rate achievable with perfect choices from the available options, used to judge how close a system gets.Evaluation & Training

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