---

name: contrib-and-extensions description: "Use Yellowbrick experimental contrib visualizers, missing-data plots, decision boundaries, pre-predicted outputs, third-party estimator wrappers, and statsmodels adapters." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Yellowbrick Contrib and Extensions

Use this sub-skill only after checking whether a stable Yellowbrick core workflow fits the request. Contrib APIs are useful but less stable: they live in specific submodules, are not hoisted from yellowbrick.contrib, may rely on optional packages, and may expose prototype behavior.

Route stable workflows first

• Classification reports, confusion matrices, ROC/PR curves, threshold tuning, and ordinary classifier score plots: use classifier visualizers first.
• Regression residual, prediction-error, Cook's distance, and alpha-selection plots: use regressor visualizers first.
• Feature ranking, PCA/manifold projections, target balance/binning, and feature-target correlation: use feature-target visualizers first.
• Clustering and cross-validation/model-selection diagnostics: use cluster and model selection first.
• Shared fit/score/show, axes, style, Agg, and saved-output patterns: read visualizer patterns.
• Package-wide install/display/scikit-learn compatibility failures: read root troubleshooting before applying contrib-specific fixes.

Stay here when the user explicitly needs yellowbrick.contrib objects, an experimental decision boundary, missing-value visualization, an adapter for a third-party estimator, already-computed predictions, or statsmodels integration.

Correct import paths

Do not import these objects from top-level yellowbrick.contrib; that package initializer does not hoist them. Use the concrete modules below:

Need	Correct import
2D feature scatter	from yellowbrick.contrib.scatter import ScatterVisualizer, scatterviz
Decision boundaries	from yellowbrick.contrib.classifier.boundaries import DecisionBoundariesVisualizer, decisionviz
Missing-value counts/locations	from yellowbrick.contrib.missing import MissingValuesBar, MissingValuesDispersion
Pre-computed predictions	from yellowbrick.contrib.prepredict import PrePredict
Third-party estimator wrapper	from yellowbrick.contrib.wrapper import ContribEstimator, wrap, classifier, regressor, clusterer
Wrapper type constants	from yellowbrick.contrib.wrapper import CLASSIFIER, REGRESSOR, CLUSTERER
statsmodels adapter	from yellowbrick.contrib.statsmodels import StatsModelsWrapper

yellowbrick.contrib.classifier also exports DecisionViz and DecisionBoundariesVisualizer, but the explicit boundaries path is the safest public guidance.

Covered objects

• ScatterVisualizer / scatterviz: bivariate feature scatter for exactly two selected features, with classes, color/colormap, markers, and alpha.
• DecisionBoundariesVisualizer / DecisionViz / decisionviz: experimental two-feature classifier decision-boundary mesh with optional scatter overlay.
• MissingValuesBar and MissingValuesDispersion: missing-value counts and missing-value positions by feature, optionally colored by target class.
• PrePredict: a scikit-learn-like estimator that returns predictions computed elsewhere and can score classifier/regressor/clusterer outputs.
• ContribEstimator, wrap, classifier, regressor, clusterer: wrappers for third-party estimators that mostly implement the scikit-learn API but do not subclass scikit-learn base classes or expose Yellowbrick-friendly type metadata.
• StatsModelsWrapper: a lightweight prototype adapter for statsmodels GLM-like estimators so Yellowbrick regressor visualizers can call fit, predict, and score.

Required read order for future agents

1. Read API reference for exact signatures, import paths, type constants, optional dependencies, and behavior limits.
2. Read workflows for safe recipes covering scatter, decision boundaries, missing values, wrappers, PrePredict, statsmodels, and headless saving.
3. If anything fails, read contrib-specific troubleshooting, then root troubleshooting for package-wide dependency and Matplotlib issues.
4. For a no-network smoke check, run contrib_smoke.py. It forces Matplotlib Agg, uses synthetic data, writes a PNG, and performs a tiny wrapper check.

Minimal operating patterns

Contrib scatter

from yellowbrick.contrib.scatter import ScatterVisualizer

viz = ScatterVisualizer(features=["feature_a", "feature_b"], classes=["no", "yes"])
viz.fit(X_two_columns, y_encoded_as_0_or_1)
viz.transform(X_two_columns)
viz.show(outpath="contrib_scatter.png", clear_figure=True)

Use features=[...] for arrays, or x="col_a", y="col_b" for named data. ScatterVisualizer accepts only two plotted features; route broader feature EDA to stable feature-target visualizers.

Decision boundaries

from sklearn.neighbors import KNeighborsClassifier
from yellowbrick.contrib.classifier.boundaries import DecisionBoundariesVisualizer

viz = DecisionBoundariesVisualizer(
    KNeighborsClassifier(n_neighbors=3),
    features=["x0", "x1"],
    classes=["class 0", "class 1"],
    step_size=0.02,          # coarser than the default for fast reports
    show_scatter=True,
)
viz.fit(X_train_2d, y_train)
viz.draw(X_test_2d, y_test)
viz.show(outpath="decision_boundary.png", clear_figure=True)

Decision boundaries are classifier-only and bivariate. Use a small sample or a coarser step_size for CI and agent-generated reports because the mesh grid can be expensive.

Wrapping non-sklearn estimators

from yellowbrick.contrib.wrapper import wrap, CLASSIFIER
from yellowbrick.classifier import ClassificationReport

model = wrap(third_party_classifier, CLASSIFIER)
viz = ClassificationReport(model, classes=class_names, is_fitted="auto")
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show(outpath="wrapped_report.png", clear_figure=True)

Set the estimator type explicitly when Yellowbrick or scikit-learn cannot infer whether the wrapped object is a classifier, regressor, clusterer, density estimator, or outlier detector. Add properties to a custom ContribEstimator subclass when a visualizer needs learned attributes such as classes_, coef_, feature_importances_, or predict_proba.

Validation checklist

Before handing off a contrib workflow:

• Confirm imports use concrete contrib submodules, not top-level yellowbrick.contrib.
• Confirm a stable core visualizer is not a better fit.
• Confirm classifier/regressor/clusterer type metadata for wrappers and PrePredict.
• Confirm decision-boundary and scatter inputs contain exactly two plotted features, with explicit feature names where arrays are used.
• Confirm class display names match encoded target order; use encoders or sorted labels deliberately.
• Confirm optional pandas or statsmodels requirements are installed before giving code that depends on them.
• Confirm headless scripts set matplotlib.use("Agg") before importing pyplot and write non-empty files with show(outpath=...).
• Ignore stale contributor example patterns that depend on missing local data, obsolete contrib modules, or deprecated NumPy aliases.