Project Title

interpret — Open-Source Machine Learning Interpretability Toolkit

Overview

InterpretML is an open-source package that brings together state-of-the-art machine learning interpretability techniques. It enables the training of interpretable glassbox models and the explanation of blackbox systems, providing insights into model behavior and individual predictions. This tool is crucial for model debugging, feature engineering, detecting fairness issues, and ensuring regulatory compliance.

Key Features

Train and use Explainable Boosting Machines (EBM), a type of interpretable model developed at Microsoft Research.
Provides detailed explanations for blackbox systems, making them understandable and editable by domain experts.
Supports various interpretability techniques, including glassbox models and local explanations.

Use Cases

Data scientists can use InterpretML for model debugging to understand why their models make specific mistakes.
Businesses can leverage it for feature engineering to improve model performance.
Regulators and compliance officers can use it to ensure models meet legal requirements in high-risk applications like healthcare and finance.

Advantages

Offers a unified platform for various interpretability techniques, simplifying the process of understanding machine learning models.
EBMs provide exact explanations and are as accurate as state-of-the-art techniques like random forests and gradient boosted trees.
Supports model transparency and trust, which is essential for human-AI cooperation and high-stakes decision-making.

Limitations / Considerations

The project is primarily focused on Python, which may limit its use for developers working in other programming languages.
As with any interpretability tool, the explanations provided are only as good as the underlying model and data.

LIME: A library that helps explain the predictions of any machine learning classifier in a locally linear way, differing in its approach to local interpretability.
SHAP: A game theoretic approach to explain the output of any machine learning model, offering a different perspective on model interpretability.
ELI5: A library for debugging machine learning classifiers and explaining their predictions, which is more focused on model-agnostic explanations.

Basic Information

GitHub: https://github.com/interpretml/interpret
Stars: 6,710
License: Unknown
Last Commit: 2025-11-16

📊 Project Information

Project Name: interpret
GitHub URL: https://github.com/interpretml/interpret
Programming Language: C++
⭐ Stars: 6,710
🍴 Forks: 771
📅 Created: 2019-05-03
🔄 Last Updated: 2025-11-16

🏷️ Project Topics

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📚 Documentation

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interpret

Project Description