A package for analyzing feature interactions in machine learning models
Project description
pymltools
A comprehensive Python toolkit for analyzing feature interactions in machine learning models, combining multiple methodologies to provide deep insights into feature relationships and their impact on model behavior.
Features
Interaction Analysis Methods
- SHAP Interaction Analysis: Leverages SHAP values to detect and quantify feature interactions
- Feature Binning Analysis: Uses optimal binning techniques to identify non-linear relationships
- Sensitivity Analysis: Implements Sobol indices to measure feature interaction effects
Key Capabilities
- Statistical significance testing for interactions
- Visualization of interaction effects
- Multiple testing correction
Installation
pip install ml-feature-toolkit
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