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name: regressor-visualizers description: "Use Yellowbrick regression diagnostics for residuals, prediction error, Cook's distance, alpha selection, and regression quick-method reports." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Yellowbrick Regressor Visualizers

Use this sub-skill when the task is to diagnose or report scikit-learn regression models with Yellowbrick. It covers model score visualizers (ResidualsPlot, PredictionError), influence diagnostics (CooksDistance), regularization-alpha diagnostics (AlphaSelection, ManualAlphaSelection), and their quick methods.

For shared visualizer lifecycle, axes, style, and headless-rendering setup, load visualizer patterns. For installation, Matplotlib backend/font, and broad dependency troubleshooting, load the root troubleshooting reference. Route feature ranking and model-selection tasks such as FeatureImportances and RFECV to cluster/model-selection.

Fast routing

User goal	Use	Why
"Are residuals random, centered, and normally distributed?"	ResidualsPlot	Plots residuals (y_pred - y) against predicted values; optional histogram or Q-Q side panel helps evaluate linear-model assumptions.
"Are predictions systematically high/low across the target range?"	PredictionError	Plots actual target y against predicted target ŷ; identity and best-fit lines expose bias, over/under-prediction, and variance.
"Which rows drive an OLS fit or look like influential outliers?"	CooksDistance	Computes Cook's distance with an internal ordinary least-squares model and marks the 4/n influence threshold.
"Which alpha did a CV linear model choose?"	AlphaSelection	Wraps RidgeCV, LassoCV, LassoLarsCV, or ElasticNetCV-style estimators and plots alpha versus stored CV error/score.
"I have a non-CV estimator with an alpha parameter."	ManualAlphaSelection	Calls cross_val_score over an explicit alpha grid for estimators that support set_params(alpha=...).

Minimal model-diagnostic workflow

import matplotlib
matplotlib.use("Agg")  # use before importing pyplot in headless sessions

from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from yellowbrick.regressor import ResidualsPlot, PredictionError

X, y = make_regression(n_samples=250, n_features=8, noise=12.0, random_state=7)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13)

residuals = ResidualsPlot(Ridge(alpha=1.0), hist="density")
residuals.fit(X_train, y_train)
residuals.score(X_test, y_test)
residuals.show(outpath="residuals.png", clear_figure=True, bbox_inches="tight")

pe = PredictionError(Ridge(alpha=1.0), shared_limits=True, bestfit=True, identity=True)
pe.fit(X_train, y_train)
pe.score(X_test, y_test)
pe.show(outpath="prediction_error.png", clear_figure=True, bbox_inches="tight")

Expected public attributes after scoring: ResidualsPlot.train_score_, ResidualsPlot.test_score_, and PredictionError.score_ contain the wrapped estimator's regression score, usually R^2.

Output/report rules

• Prefer class visualizers for reports because show(outpath=..., clear_figure=True, bbox_inches="tight") saves and closes figures in a predictable way.
• Quick methods are fine for notebooks or one-off plots. If you need a file, call the quick method with show=False, then call viz.show(outpath=...) on the returned visualizer.
• For residual diagnostics, score the training split first through fit(X_train, y_train), then overlay test points with score(X_test, y_test). Yellowbrick labels train and test R^2 separately.
• For ResidualsPlot, choose either hist or qqplot; hist=True and qqplot=True together raise a YellowbrickValueError.
• For reusable automated reports, set a non-interactive Matplotlib backend (Agg) before importing matplotlib.pyplot or Yellowbrick visualizers.

Reference map

• API reference — concrete signatures, import paths, learned attributes, and quick-method caveats.
• Workflows — residual-vs-prediction-error selection, train/test overlays, alpha tuning, Cook's distance, and pipeline patterns.
• Troubleshooting — regressor type errors, fitted/refit decisions, Matplotlib issues, alpha-estimator compatibility, and report-output failures.
• Smoke helper — deterministic synthetic-data Agg smoke that writes regression diagnostic PNGs without network access.

Validate this sub-skill in a target environment

python skills/disco/yellowbrick/sub-skills/regressor-visualizers/scripts/regression_smoke.py --outdir /tmp/yellowbrick-regression-smoke
ls -1 /tmp/yellowbrick-regression-smoke

Expected output files include residuals_density.png, residuals_qq.png, prediction_error.png, cooks_distance.png, alpha_selection.png, manual_alpha_selection.png, and manifest.json.