Interpretable Machine Learning
- Benefits of IML
- Interpretability as the ability to understand how a machine learning model works
- Interpretability is better than explainability.
- Black-box models can be interpretable if viewed from a different perspective.
- Glass box models gaining popularity in IML and provide the benefits of both white-box and black-box models.
- Various modeling approaches related to IML, including rule-based models, decision trees, local linear models, and additive models.
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