kuva pr
Precision-recall curve — evaluates binary classifiers when class imbalance makes ROC curves optimistic.
Input: one row per sample with a numeric score column and a binary label column (1 = positive, 0 = negative).
| Flag | Default | Description |
|---|---|---|
--score-col <COL> | 0 | Classifier score column (higher = more positive) |
--label-col <COL> | 1 | True label column (1/0 or true/false) |
--color-by <COL> | — | Group column; one curve per unique value |
--no-baseline | off | Hide the no-skill (prevalence) baseline |
--auc-label | off | Append AUC-PR value to each curve's legend entry |
--legend <LABEL> | — | Add a legend |
kuva pr data.tsv --score-col score --label-col label --auc-label
kuva pr data.tsv --score-col score --label-col label \
--color-by model --auc-label --legend "Model" \
--title "Precision-Recall Curves"
See also: Shared flags — output, appearance, axes.