InterpretML - Understand Models and Enable Responsible Machine Learning with InterpretML
InterpretML is a comprehensive open source toolkit designed to help developers, data scientists, and business stakeholders understand machine learning models, debug issues, explain predictions, and ensure compliance with regulatory requirements.
InterpretML Product Information
What is InterpretML?
InterpretML provides state-of-the-art techniques to explain model behavior, supports various model types and algorithms during training and inferencing, and offers a community-driven open source approach to model interpretability. It offers a wide range of global and local interpretability techniques, enabling users to explore model performance, understand model behavior, and gain insights into the impact of input features on predictions.
How to Use InterpretML?
Easily access interpretability techniques through an open unified API set and interactive visuals. Choose from a wide array of explainers and techniques to understand your models, explore model attributes, and analyze the impact of changes to input features on predictions.
Core Features of InterpretML
- State-of-the-art interpretability techniques
- Comprehensive support for multiple model types and algorithms
- Wide range of global and local interpretability techniques
- Interactive visualizations
- Comparative model analysis
Use Cases of InterpretML
- Understanding model behavior and performance
- Debugging machine learning models
- Explaining model predictions
- Ensuring compliance with regulatory requirements
FAQ about InterpretML
Who can benefit from InterpretML?
InterpretML benefits data scientists, auditors, business leaders, and researchers by providing tools to understand, debug, and explain machine learning models.
How can users contribute to InterpretML?
Users are encouraged to contribute feedback, algorithms, ideas, and more to evolve the toolkit together.
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