---

name: feature-target-visualizers description: "Use Yellowbrick feature and target visualizers for feature ranking, projection diagnostics, class balance, target binning, and feature-target correlation." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Feature and Target Visualizers

Use this sub-skill when the task is to diagnose raw features, projected feature space, or the target variable before or alongside modeling with Yellowbrick. It covers Rank1D, Rank2D, RadialVisualizer, ParallelCoordinates, PCA, Manifold, JointPlot, ClassBalance, BalancedBinningReference, and FeatureCorrelation.

Route first

• For common fit/transform/show, Matplotlib backend, style, axes, and headless saving patterns, read shared visualizer patterns.
• For API signatures and imports, read API reference.
• For visualizer choice and task workflows, read workflows.
• For feature/target-specific failures, read troubleshooting; for package-wide install/display issues, also read root troubleshooting.

Use this sub-skill for

• Ranking or screening columns: Rank1D for univariate distribution scoring; Rank2D for pairwise correlation/covariance-style structure.
• Class-separability plots in raw feature space: RadialVisualizer/RadViz and ParallelCoordinates.
• Dimensionality reduction plots: PCA for fast linear projections; Manifold for non-linear embeddings when the cost and algorithm limitations are acceptable.
• Pairwise or feature-vs-target distribution plots: JointPlot.
• Target diagnostics: ClassBalance, BalancedBinningReference, and FeatureCorrelation.
• Headless smoke checks for feature/target visualizer usage with scripts/feature_target_smoke.py.

Route elsewhere

• Route cross-validation, validation/learning curves, feature importance, RFECV, clustering elbow/silhouette/intercluster distance, and other model-selection curves to cluster-model-selection.
• Route text-specific feature visualizers and dataset downloader/cache questions to text-and-datasets.
• Route model-fitted classifier reports, confusion matrices, ROC/PR curves, prediction-error bars, and threshold tuning to classifier-visualizers. Stay here for ClassBalance; cross-link to classifier visualizers when the user proceeds from target imbalance to model diagnostics.

Intake checklist

Before giving code or debugging advice, identify:

1. Whether the task is about X features, y target, or both.
2. Data shape: feature visualizers generally need X.shape == (n_samples, n_features); target-only visualizers need a one-dimensional y.
3. Feature names and class names: prefer explicit features, labels, or classes lists when arrays are used or when optional pandas support is unavailable.
4. Whether y is classification-like or continuous; force target_type="discrete" or target_type="continuous" for projection visualizers when automatic inference is ambiguous.
5. Runtime constraints: use PCA before manifold learning for quick checks; sample rows or features for expensive plots; set a non-interactive Matplotlib backend for headless execution.
6. Output requirement: for files or CI reports, use the class API and visualizer.show(outpath=..., clear_figure=True).

Minimal operating pattern

import matplotlib
matplotlib.use("Agg")  # set before pyplot in headless jobs
import matplotlib.pyplot as plt

from yellowbrick.features import Rank2D

fig, ax = plt.subplots(figsize=(7, 6))
viz = Rank2D(ax=ax, features=feature_names, algorithm="pearson")
viz.fit(X, y)
viz.transform(X)
viz.show(outpath="rank2d.png", clear_figure=True)
plt.close(fig)

Use fit_transform(X, y) for PCA and for Manifold algorithms that do not support a separate transform() step. Use separate target visualizers outside sklearn prediction pipelines because they fit directly on y rather than on (X, y) estimator data.