DistrictDataLabs/yellowbrick

Health v2 · maintenance only

Adoption not part of the score

4400 stars · 569 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Yellowbrick is a Python library of visual diagnostic tools called Visualizers that extend the scikit-learn API using matplotlib. It helps users visually steer and diagnose the machine learning model selection process.

Use cases

• visualize machine learning model performance
• diagnose model selection with plots
• create scikit-learn compatible visualizations
• plot feature importance and confusion matrices
• compare estimators visually
• teach machine learning with visual diagnostics

When to choose

• you use scikit-learn and want visual diagnostics for model selection
• you need quick plots like residual, ROC, or learning curves integrated into your ML workflow
• you want a matplotlib-based visualization suite following the scikit-learn API

When to avoid

• you need general-purpose plotting outside machine learning workflows
• you don't use scikit-learn estimators
• you need interactive dashboards rather than static matplotlib visualizations

Facets

library · maturity active

data-visualization machine-learning benchmarking machine-learning data-science data-visualization python scikit-learn matplotlib model-selection visualizers diagnostics

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DistrictDataLabs/yellowbrick")