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In the beginning, machines learned in darkness, and data scientists struggled in the void to explain them. InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.
Features
- Model debugging
- Feature Engineering
- Detecting fairness issues
- Documentation available
- Human-AI cooperation
- Regulatory compliance
- High-risk applications
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow InterpretML
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