# 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.

## Install

Save in your project root:

# InterpretML — Interpretable Machine Learning by Microsoft

## Quick Use
```bash
pip install interpret
python -c "
from interpret.glassbox import ExplainableBoostingClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
ebm = ExplainableBoostingClassifier()
ebm.fit(X_train, y_train)
print(f'Accuracy: {ebm.score(X_test, y_test):.3f}')
from interpret import show
ebm_global = ebm.explain_global()
show(ebm_global)
"
```

## Introduction
InterpretML 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.

## What InterpretML Does
- Trains glass-box models like EBM, linear models, and decision trees
- Explains any black-box model with SHAP, LIME, and partial dependence
- Provides interactive visualizations of feature importances and effects
- Supports both classification and regression tasks
- Enables comparison of multiple explanations side by side

## Architecture Overview
InterpretML 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.

## Self-Hosting & Configuration
- Install via pip; the package includes all glass-box models
- Use ExplainableBoostingClassifier or Regressor as drop-in sklearn estimators
- Set max_bins and interactions to control EBM complexity
- For black-box explanations, wrap any predict function with ShapKernel or LimeTabular
- Launch the interactive dashboard with show() in a Jupyter environment

## Key Features
- EBM matches gradient boosting accuracy with full interpretability
- Unified API across glass-box and black-box explanation methods
- Interactive Plotly-based dashboards for exploring feature effects
- Pairwise interaction detection built into the EBM training loop
- Differential privacy support for training on sensitive data

## Comparison with Similar Tools
- **SHAP** — focuses on Shapley value explanations for any model; InterpretML includes SHAP plus its own glass-box models
- **LIME** — local explanation technique; InterpretML integrates LIME alongside other methods
- **Alibi** — strong on counterfactual and anchor explanations; InterpretML focuses on feature-level interpretation
- **Captum** — PyTorch-specific attribution methods; InterpretML is framework-agnostic

## FAQ
**Q: What is an Explainable Boosting Machine?**
A: 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.

**Q: Does EBM work with large datasets?**
A: Yes. EBM scales well and supports parallel training via the n_jobs parameter.

**Q: Can I use InterpretML with deep learning models?**
A: Yes. The black-box explainers work with any model that has a predict or predict_proba method.

**Q: Is InterpretML production-ready?**
A: Yes. EBM models can be serialized with joblib or pickle and served like any scikit-learn model.

## Sources
- https://github.com/interpretml/interpret
- https://interpret.ml/docs

---
Source: https://tokrepo.com/en/workflows/asset-abe0118d
Author: AI Open Source