Understand Models.
Build Responsibly.

A toolkit to help understand models and enable responsible machine learning

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 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


Explore overall model behavior and find top features affecting model predictions using global feature importance


Explain an individual prediction and find features contributing to it using local feature importance


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.


    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.


    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!