InterpretML
An open-source Python package for training interpretable glassbox models and explaining blackbox machine learning systems.
What is InterpretML?
InterpretML is an open-source Python package that provides a unified framework for machine learning interpretability. It allows data scientists to train inherently interpretable 'glassbox' models like the Explainable Boosting Machine and to generate explanations for complex 'blackbox' models using techniques like SHAP and LIME. It solves the critical problem of understanding and trusting ML models in high-risk domains such as healthcare, finance, and judicial applications.
Data scientists, ML engineers, and researchers who need to build, debug, audit, or deploy machine learning models where transparency, fairness, and regulatory compliance are required.
Developers choose InterpretML because it combines state-of-the-art accuracy with full interpretability in a single package, eliminating the traditional trade-off. Its flagship Explainable Boosting Machine matches the performance of random forests and gradient boosting while providing exact, visual explanations, and it unifies multiple explanation techniques under a consistent API.
Overview
Fit interpretable models. Explain blackbox machine learning.
Use Cases
Best For
- Building high-stakes predictive models in regulated industries like finance and healthcare
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