---
id: yellowbrick
version: "1.5"
license: Apache 2
license_treatment: permissive
maintenance: dormant
---

# yellowbrick — A suite of visual analysis and diagnostic tools for machine learning.

License: permissive · Maintenance: dormant · Downloads: 593.9K/mo

## What it is and what it does

Yellowbrick wraps estimators with a Visualizer API that generates diagnostic plots for the model selection workflow. It implements visualizers for classification (confusion matrices, ROC curves, precision-recall plots), clustering (silhouette analysis, elbow curves), feature analysis (PCA projections, feature importance rankings), regression (residual plots, prediction error analysis), and text data (term frequency, t-SNE corpus plots). Each visualizer learns from data like a transformer or model, integrating into existing workflows.

The library's core strength is reducing the friction of exploratory model diagnostics—instead of writing matplotlib code to plot cross-validation scores, learning curves, or class balance, you instantiate a visualizer, fit it to your data, and call show(). It depends on matplotlib for rendering, scipy and numpy for computation, and scikit-learn for the estimator interface. The package is stable and production-ready but dormant; no active development has occurred since mid-2022.

Use it for:

- Visualize confusion matrices and classification reports to diagnose precision, recall, and F1 imbalances across classes
- Plot learning curves to determine whether a model would benefit from more training data or reduced complexity
- Generate ROC and precision-recall curves to evaluate binary and multiclass classifier thresholds
- Identify optimal cluster count using silhouette scores or elbow detection in clustering workflows
- Rank feature importance and perform recursive feature elimination to guide feature engineering
- Inspect residuals and prediction errors in regression models to detect systematic model failures

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Yellowbrick provides scikit-learn-integrated visualizers for model selection, diagnostic analysis, and feature engineering in machine learning workflows.

Yes, if you are working with scikit-learn and need quick diagnostic visualizations without writing custom matplotlib code. The low install friction and permissive license make it a safe addition. However, the dormant maintenance status means you should verify compatibility with your specific scikit-learn and matplotlib versions before relying on it in production—no active support is available for breakage.

## Install

pip install yellowbrick
uv add yellowbrick
poetry add yellowbrick

## Installing yellowbrick

Before you install: Low friction install with standard scientific Python stack dependencies (matplotlib, scipy, scikit-learn, numpy). Maintenance is dormant—last release was 2022-08-21 and no commits since 2025-02-19, so expect no active bug fixes or feature development, though the codebase remains stable.

License in practice: Apache 2 permissive license allows commercial and private use without restriction, making it safe for most projects.

Quickstart:

pip install yellowbrick

from yellowbrick.classifier import ConfusionMatrix

visualizer = ConfusionMatrix(estimator)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.show()

Verify before relying:

- Whether dormant status means breaking changes in newer scikit-learn or matplotlib versions are unaddressed
- Compatibility with Python versions beyond 3.9 (classifiers list ends at 3.9, requires_python allows up to 3.99)

## Package facts

- License: Apache 2 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 593.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

scikit-learn visualization, machine learning model diagnostics, feature importance visualization, classification model evaluation plots, hyperparameter tuning visualization, clustering analysis charts, learning curve plots, scikit-learn-integration, model-diagnostics, exploratory-analysis

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