[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"workflow-asset-abe0118d":3,"seo:featured-workflow:abe0118d-4ddd-11f1-9bc6-00163e2b0d79:en":84,"workflow-related-asset-abe0118d-abe0118d-4ddd-11f1-9bc6-00163e2b0d79":85},{"id":4,"uuid":5,"slug":6,"title":7,"description":8,"author_id":9,"author_name":10,"author_avatar":11,"token_estimate":12,"time_saved":12,"model_used":11,"fork_count":12,"vote_count":12,"view_count":13,"parent_id":12,"parent_uuid":11,"lang_type":14,"steps":15,"tags":21,"has_voted":27,"visibility":13,"share_token":11,"is_featured":12,"content_hash":28,"asset_kind":29,"target_tools":30,"install_mode":34,"entrypoint":18,"risk_profile":35,"dependencies":37,"verification":43,"agent_metadata":46,"agent_fit":59,"trust":71,"provenance":80,"created_at":82,"updated_at":83},3248,"abe0118d-4ddd-11f1-9bc6-00163e2b0d79","asset-abe0118d","InterpretML — Interpretable Machine Learning by Microsoft","InterpretML provides glass-box models like Explainable Boosting Machines and black-box explainers in a unified API, helping data scientists understand why models make specific predictions.","8a911193-3180-11f1-9bc6-00163e2b0d79","AI Open Source","",0,1,"en",[16],{"id":17,"step_order":13,"title":18,"description":11,"prompt_template":19,"variables":11,"depends_on":20,"expected_output":11},3811,"InterpretML Explainability","# InterpretML — Interpretable Machine Learning by Microsoft\n\n## Quick Use\n```bash\npip install interpret\npython -c \"\nfrom interpret.glassbox import ExplainableBoostingClassifier\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nX, y = load_breast_cancer(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nebm = ExplainableBoostingClassifier()\nebm.fit(X_train, y_train)\nprint(f'Accuracy: {ebm.score(X_test, y_test):.3f}')\nfrom interpret import show\nebm_global = ebm.explain_global()\nshow(ebm_global)\n\"\n```\n\n## Introduction\nInterpretML is an open-source library from Microsoft Research that unifies interpretable (glass-box) models and explainability techniques (black-box) under a single API. Its flagship model, the Explainable Boosting Machine (EBM), achieves accuracy comparable to gradient boosting while remaining fully interpretable.\n\n## What InterpretML Does\n- Trains glass-box models like EBM, linear models, and decision trees\n- Explains any black-box model with SHAP, LIME, and partial dependence\n- Provides interactive visualizations of feature importances and effects\n- Supports both classification and regression tasks\n- Enables comparison of multiple explanations side by side\n\n## Architecture Overview\nInterpretML defines an Explainer interface with explain_global and explain_local methods. Glass-box models implement both training and explanation natively. Black-box explainers wrap external models and produce explanations via perturbation or gradient methods. All explanations are rendered as interactive Plotly dashboards.\n\n## Self-Hosting & Configuration\n- Install via pip; the package includes all glass-box models\n- Use ExplainableBoostingClassifier or Regressor as drop-in sklearn estimators\n- Set max_bins and interactions to control EBM complexity\n- For black-box explanations, wrap any predict function with ShapKernel or LimeTabular\n- Launch the interactive dashboard with show() in a Jupyter environment\n\n## Key Features\n- EBM matches gradient boosting accuracy with full interpretability\n- Unified API across glass-box and black-box explanation methods\n- Interactive Plotly-based dashboards for exploring feature effects\n- Pairwise interaction detection built into the EBM training loop\n- Differential privacy support for training on sensitive data\n\n## Comparison with Similar Tools\n- **SHAP** — focuses on Shapley value explanations for any model; InterpretML includes SHAP plus its own glass-box models\n- **LIME** — local explanation technique; InterpretML integrates LIME alongside other methods\n- **Alibi** — strong on counterfactual and anchor explanations; InterpretML focuses on feature-level interpretation\n- **Captum** — PyTorch-specific attribution methods; InterpretML is framework-agnostic\n\n## FAQ\n**Q: What is an Explainable Boosting Machine?**\nA: EBM is a generalized additive model with pairwise interactions trained via cyclic gradient boosting. Each feature's effect is learned independently, making the model fully inspectable.\n\n**Q: Does EBM work with large datasets?**\nA: Yes. EBM scales well and supports parallel training via the n_jobs parameter.\n\n**Q: Can I use InterpretML with deep learning models?**\nA: Yes. The black-box explainers work with any model that has a predict or predict_proba method.\n\n**Q: Is InterpretML production-ready?**\nA: Yes. 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