Data as of Aug 16, 2026 · Based on 3,131,739 AI responses across 10,525 prompts · See how Parse measures this
InterpretMLinterpret.mlInterpretML is an open-source toolkit that helps developers, data scientists, and business stakeholders understand machine learning models through interpretability techniques. It supports both glass box models like Explainable Boosting Machines and black box explainers such as LIME and SHAP, enabling debugging, prediction explanation, and regulatory compliance.
Words AI uses
AI reaches for open-source · interpretable · interactive when it describes InterpretML.
Sources
interpret.ml shapes more of what AI says about InterpretML than any other source, at 15% of its citations.
data.world · blog.gopenai.com · parse.gl · arxiv.org
The market map
Explainable AI & Model Monitoring Platforms →Where AI ranks InterpretML
Excerpts where InterpretML appeared in the AI's answer
InterpretML (by Microsoft) - Best for: Combining glass-box (inherently interpretable) models with powerful black-box explainers.
InterpretML / Interpret Community : An open-source Python package hosted by Microsoft that balances explainability and glass-box modeling.
Excerpts where InterpretML appeared in the AI's answer
InterpretML (Microsoft): A comprehensive open-source toolkit developed by Microsoft that unifies various black-box explainers
InterpretML: Best for technical teams needing to generate interactive, user-friendly reports for stakeholders.
Excerpts where InterpretML appeared in the AI's answer
InterpretML (Microsoft) — interpretable models plus black-box explanation methods.
InterpretML : An open-source toolkit backed by Microsoft that exposes both inherently interpretable "glass-box" models and post-hoc black-box explainers like SHAP and LIME with clean visualization dashboards.
