---

name: estimators description: "Route feature-importance and learning-curve requests to scikitplot.estimators." disable-model-invocation: true metadata: disco-role: operating license: MIT

Estimators

Use this sub-skill for the estimator-oriented plots in scikitplot.estimators: feature importances and learning curves.

Route here

• Plot feature importances from a fitted estimator that exposes feature_importances_.
• Plot learning curves from an estimator plus X, y, CV choices, and scoring settings.
• Reuse or compose Matplotlib axes with ax=; both functions return the axes they drew on.
• Validate the route quickly with scripts/estimators_smoke.py.

Reroute

• Confusion matrix, ROC, precision-recall, KS, calibration, cumulative gain, lift, or silhouette: ../metrics/SKILL.md.
• Elbow-curve workflows or clusterer-specific routing: ../clustering/SKILL.md.
• Bound-method wrappers from the legacy factory layer, including injected estimator methods: ../legacy-factories/SKILL.md.
• PCA or other dimensionality-reduction plots: not handled here.

Start fast

1. Confirm the estimator contract.
   • plot_feature_importances requires feature_importances_.
   • plot_learning_curve passes an estimator through sklearn.model_selection.learning_curve.
2. Pick the right options.
   • order accepts only ascending, descending, or None.
   • cv, shuffle, random_state, train_sizes, n_jobs, and scoring are forwarded to learning_curve.
3. Reuse axes explicitly when composing figures.
4. Run the smoke script for a tiny deterministic check:

python scripts/estimators_smoke.py

References

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md
• scripts/estimators_smoke.py