InterpretML Documentation Contribute GitHub Understand Models. Build Responsibly. A toolkit to help understand models and enable responsible machine learning Get Started Learn More State-of-the-art techniques to explain model behavior Comprehensive support for multiple types of models and algorithms, during training and inferencing Community driven open source toolkit Why InterpretML? Model Interpretability Model interpretability helps developers, data scientists and business stakeholders in the organization gain a comprehensive understanding of their machine learning models. It can also be used to debug models, explain predictions and enable auditing to meet compliance with regulatory requirements. Ease of use Access state-of-the-art interpretability techniques through an open unified API set and rich visualizations. Flexible and customizable Understand models using a wide range of explainers and techniques using interactive visuals. Choose your algorithm and easily experiment with combinations of algorithms. Comprehensive capabilities Explore model attributes such as performance, global and local features and compare multiple models simultaneously. Run what-if analysis as you manipulate data and view the impact on the model. Types of Models Supported Glass-Box Black-Box Glass-box models are interpretable due to their structure. Examples include: Explainable Boosting Machines (EBM), Linear models, and decision trees. Glass-box models produce lossless explanations and are editable by domain experts. Black-box models are challenging to understand, for example deep neural networks. Black-box explainers can analyze the relationship between input features and output predictions to interpret models. Examples include LIME and SHAP. Wide Variety of Techniques Global Explore overall model behavior and find top features affecting model predictions using global feature importance Local Explain an individual prediction and find features contributing to it using local feature importance Subset Explain a subset of predictions using group feature importance Feature Impact See how changes to input features impact predictions with techniques like what-if analysis What You Can Do With InterpretML Explore Your Data and Model Performance Understand how model performance changes for different subsets of data and compare multiple models Explore model errors Analyze dataset statistics and distributions Gain Model Understanding Explore global and local explanations Filter data to observe global and local feature importance Run what-if analysis to see how model explanations change if you edit datapoints’ features Who Can Benefit from InterpretML? Data Scientists Understand models, debug or uncover issues and explain your model to other stakeholders. Auditors Validate a model before it is deployed and audit it post-deployment. Business Leaders Understand how models behave, in order to provide transparency about predictions to customers. Researchers Easily integrate with new interpretability techniques and compare against other algorithms. Getting Started Install InterpretML Contribute to InterpretML We encourage you to join the effort and contribute feedback, algorithms, ideas and more, so we can evolve the toolkit together! Contribute Resources Learn More About InterpretML InterpretML documentation Interpretability for Tabular Data Counterfactual Example Analysis Contact Us Copyright © 2023 The InterpretML Contributors GitHub