# DistrictDataLabs/yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection.

Repository: https://github.com/DistrictDataLabs/yellowbrick
Canonical: https://ross.abutalabs.com/products/yellowbrick
Homepage: http://www.scikit-yb.org/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, visual-analysis, model-selection, visualization, scikit-learn, visualizer, matplotlib, python, estimator, anaconda
Last push: 2025-02-19T20:59:35+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 7, release rhythm 8, longevity 100
- inputs: {"age_days": 3759, "days_push": 560, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4400, forks 569 (observed 2026-08-28T04:08:48.283560+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: data-visualization, machine-learning, benchmarking
- domain: machine-learning, data-science, data-visualization
- platform: python
- tags: scikit-learn, matplotlib, model-selection, visualizers, diagnostics

## Member repositories
- DistrictDataLabs/yellowbrick (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.283560+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:08.719271+00:00, confidence not recorded.
  - readme: https://github.com/DistrictDataLabs/yellowbrick (fetched 2026-08-28T04:08:48.283560+00:00, sha 566b7ca362cc)
  - registry_pypi: https://pypi.org/pypi/yellowbrick/json (fetched 2026-08-29T09:09:31.784048+00:00, sha 1f9fe4b7261c)
- Data as of 2026-08-30T08:39:29.467469+00:00.