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ExplainerDashboardexplainerdashboard.readthedocs.ioExplainerDashboard is a Python library for quickly building interactive dashboards to analyze and explain the predictions and behavior of machine learning models (scikit-learn compatible, including XGBoost, CatBoost and LightGBM). It provides tools to investigate SHAP values, permutation importances, interaction effects, partial dependence plots, and various performance plots, even enabling viewing individual decision trees inside a random forest. You construct an explainer from your model and test data, then create an ExplainerDashboard (and optionally an ExplainerHub to host multiple dashboards), or use an InlineExplainer to view components in notebooks, enabling interactive explainable AI apps without web development.
Sources
explainerdashboard.readthedocs.io shapes more of what AI says about ExplainerDashboard than any other source, at 15% of its citations.
pypi.org · towardsdatascience.com · airev.us · github.com
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ExplainerDashboard - Best for: Instantly turning complex models (XGBoost, LightGBM, CatBoost, Scikit-Learn) into interactive web apps with minimal code.
ExplainerDashboard: An open-source Python module that allows you to quickly build interactive web dashboards explaining machine learning models.
