oegedijk/explainerdashboard

Health v2 · maintenance only

Adoption not part of the score

2510 stars · 348 forks observed · 2026-08-28

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

A Python library for quickly building interactive Explainable AI dashboards that explain the inner workings of scikit-learn compatible machine learning models. It provides interactive plots for model performance, feature importances, SHAP values, partial dependence, and individual prediction explanations, deployable as Dash web apps or static HTML.

Use cases

• explain a blackbox machine learning model with an interactive dashboard
• show shap values and feature contributions for individual predictions
• visualize feature importances and permutation importance for a model
• build a what-if analysis tool for model predictions
• deploy a model explanation web app for stakeholders
• export model explanation dashboards to static html in ci/cd
• combine multiple model dashboards into a single hub

When to choose

• you have a scikit-learn, xgboost, lightgbm, or catboost model and need explainability visuals with minimal code
• you want an interactive web dashboard or notebook exploration of model behavior without building a frontend
• you need SHAP values, partial dependence, and decision tree visualizations in one tool

When to avoid

• you need explainability for non-tabular models like images or text transformers
• you want a fully custom dashboard UI beyond the library's modular components
• your model is not scikit-learn compatible

Facets

library · maturity active

data-visualization machine-learning web-framework charts machine-learning data-science data-visualization web-development python cross-platform explainable-ai shap dash plotly model-interpretability xai scikit-learn feature-importance web-server

Member repositories

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markdown · JSON · MCP: product_card(name="oegedijk/explainerdashboard")