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name: metrics description: "Routes scikit-plot metric-curve requests for confusion matrices, ROC, precision-recall, KS, calibration, gain, lift, and silhouette plots." disable-model-invocation: true metadata: disco-role: operating license: MIT

Metrics

Use this sub-skill for the current scikitplot.metrics plotting functions. These functions consume labels, predictions, probabilities, scores, or cluster labels and return Matplotlib Axes objects.

Route here

• Confusion matrices: plot_confusion_matrix.
• Multiclass or binary ROC curves: plot_roc; legacy code may still call plot_roc_curve.
• Multiclass or binary precision-recall curves: plot_precision_recall; legacy code may still call plot_precision_recall_curve.
• Binary probability diagnostics: plot_ks_statistic, plot_calibration_curve, plot_cumulative_gain, plot_lift_curve.
• Silhouette analysis from X plus cluster labels: plot_silhouette.
• Quick validation of the metric family: scripts/metrics_smoke.py.

Inputs to check first

1. For confusion matrices, provide y_true and y_pred with aligned sample order.
2. For ROC and precision-recall, pass a 2-D probability matrix shaped (n_samples, n_classes).
3. For KS, cumulative gain, lift, and calibration, use binary targets unless the function explicitly supports multiclass.
4. For silhouette, provide the feature matrix and one cluster label per sample.
5. Pass ax= when composing figures; every route should return the axes it drew on.

Common decisions

• Use labels, true_labels, and pred_labels to reorder or subset confusion-matrix axes.
• Use plot_micro, plot_macro, and classes_to_plot on the current ROC/PR APIs instead of the older curves tuple where possible.
• Use n_bins for calibration only after confirming enough binary samples per bin.
• Keep probability columns in the same class order used by the fitted classifier.
• Set a non-interactive Matplotlib backend such as Agg for automation or smoke checks.

Reroute

• Feature importance or learning curves: ../estimators/SKILL.md.
• Elbow-curve cluster sweeps: ../clustering/SKILL.md.
• PCA variance or 2-D projection: ../decomposition/SKILL.md.
• Factory-injected bound methods or deprecated scikitplot.plotters: ../legacy-factories/SKILL.md.

Read next

• references/api-reference.md for verified signatures, input constraints, and deprecated aliases.
• references/workflows.md for concrete call patterns by plot family.
• references/troubleshooting.md for binary-only restrictions, shape errors, label validation, and compatibility failures.
• scripts/metrics_smoke.py for a tiny Agg-backed smoke run covering the main metric plot families.