MAIF/shapash

Health v2 · maintenance only

Adoption not part of the score

3251 stars · 387 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Shapash is a Python library that makes machine learning models interpretable and understandable through clear visualizations, a webapp, and audit reports. It builds on SHAP and LIME backends to provide global and local explainability for models like CatBoost, XGBoost, LightGBM, and scikit-learn.

Use cases

• explain machine learning model predictions to non-technical stakeholders
• visualize global and local feature importance
• generate an audit report for a data science project
• build a webapp to explore model explanations
• summarize local explanations for end users
• check quality of explainability methods
• interpret xgboost or lightgbm model outputs

When to choose

• you need user-friendly, labeled visualizations of SHAP/LIME explanations
• you want to share model insights with both data scientists and business users
• you need documentation or audit reports for model transparency
• you use common Python ML stacks like sklearn, XGBoost, LightGBM, or CatBoost

When to avoid

• you need explainability outside the Python ecosystem
• your model is not among the supported frameworks and you cannot provide a custom backend
• you only need raw SHAP values without summarization or visualization

Facets

library · maturity active

data-visualization machine-learning nlp machine-learning data-science data-visualization artificial-intelligence python explainability interpretability shap lime xai model-auditing webapp ethical-ai

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markdown · JSON · MCP: product_card(name="MAIF/shapash")