InterpretML is an open-source toolkit for understanding model behavior and enabling responsible machine learning. It supports both glass-box models that are inherently interpretable, like decision trees, and black-box models like neural networks, through a unified API covering global, local, subset, and feature-impact explanation techniques.
The unified API is the practical win – rather than learning a separate tool for every interpretability technique, teams get global explanations (how the model behaves overall), local explanations (why it made one specific prediction), and interactive what-if analysis through one consistent interface. That matters directly for regulatory compliance and internal auditing, where being able to explain a specific decision isn’t optional.
This is a Python library installed from GitHub, not a hosted product – there’s no signup, dashboard, or account to create. It’s free, Microsoft-maintained, and aimed squarely at data scientists, auditors, and researchers who need to explain model predictions rather than just generate them.
