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name: yellowbrick description: "Use Yellowbrick visual diagnostics for scikit-learn models, feature/target analysis, clustering, model selection, text visualizers, datasets, styles, and contrib extensions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Yellowbrick Repo Skill

Use this skill when a task asks how to use Yellowbrick, troubleshoot Yellowbrick visualizers, create saved diagnostic plots for scikit-learn workflows, load Yellowbrick example datasets, or adapt Yellowbrick-style visual checks in a headless agent environment.

Yellowbrick visualizers usually wrap scikit-learn estimators or transform data and render Matplotlib figures. Start from the shared lifecycle reference, then open the focused sub-skill for the task family.

Quick install and import check

For the repository snapshot covered by this skill, use a Python scientific stack with Matplotlib and scikit-learn. Yellowbrick 1.5 has no GPU requirement.

python -m pip install yellowbrick
python - <<'PY'
import yellowbrick
print(yellowbrick.__version__)
PY

If normal scikit-learn classifiers or regressors are rejected as the wrong estimator type, read references/troubleshooting.md; this snapshot works best with a pre-1.4 scikit-learn compatibility stack.

For headless agents and CI, set a non-interactive backend before importing pyplot or running bundled smoke scripts:

import matplotlib
matplotlib.use("Agg", force=True)

Shared workflow

1. Choose a visualizer from the route map below.
2. Prepare X, y, class labels, feature names, and any train/test split.
3. Instantiate the visualizer, often with a scikit-learn estimator and ax or style arguments.
4. Call the Yellowbrick lifecycle method: fit, transform/fit_transform, score, then show(outpath=...) for saved reports.
5. Validate with a bundled smoke helper when adapting a workflow in a new environment.

Read references/visualizer-patterns.md for lifecycle, axes, style, pipeline, and headless rendering details. Read references/troubleshooting.md for install/import, optional dependency, dataset, Matplotlib, and estimator-type failures. Read references/testing-and-validation.md when creating a smoke check or comparing against native Yellowbrick tests.

Route map

• Classification reports, confusion matrices, ROC/PR curves, threshold tuning, class prediction errors, and class balance: read sub-skills/classifier-visualizers/SKILL.md.
• Regression residuals, prediction error, Cook's distance, and alpha selection: read sub-skills/regressor-visualizers/SKILL.md.
• Feature ranking, RadViz, parallel coordinates, PCA, manifold projections, joint plots, target binning, class balance, and feature-target correlation: read sub-skills/feature-target-visualizers/SKILL.md.
• Clustering diagnostics, k selection, silhouette/intercluster plots, validation/learning curves, CV scores, RFECV, feature importances, and dropping curves: read sub-skills/cluster-model-selection/SKILL.md.
• Yellowbrick dataset loaders/cache controls and text visualizers such as frequency distribution, t-SNE, UMAP, dispersion, word correlation, and POS tags: read sub-skills/text-and-datasets/SKILL.md.
• Experimental contrib visualizers, missing-value plots, decision boundaries, third-party estimator wrappers, pre-predicted outputs, and statsmodels adapters: read sub-skills/contrib-and-extensions/SKILL.md.

Bundled shared script

Run scripts/check_yellowbrick_visualizer.py --outdir <dir> to verify that the current environment can import Yellowbrick, use Matplotlib Agg, and create small classifier, regressor, and cluster diagnostic PNGs from synthetic data. This script is safe, deterministic, offline, and independent of the original repository checkout.

Provenance and freshness

Read references/repo-provenance.md before deciding whether this skill is current for another checkout. If the source commit, package version, public API, or dirty-state baseline differs, refresh the repo skill before relying on exact signatures or compatibility notes.