boruta_py
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
What is boruta_py?
BorutaPy is a Python implementation of the Boruta all-relevant feature selection algorithm that identifies all features carrying information usable for prediction. It provides a scikit-learn compatible interface for selecting features that contribute to understanding data-generating phenomena, rather than just finding a minimal optimal subset for a specific classifier.
Data scientists, machine learning engineers, and researchers working with high-dimensional datasets who need comprehensive feature selection for model interpretation and understanding underlying data patterns.
Developers choose BorutaPy because it offers a more comprehensive approach to feature selection than minimal-optimal methods, provides a familiar scikit-learn interface, includes performance improvements over the original R implementation, and offers flexible statistical corrections suitable for various data types including biological data.
Overview
Python implementations of the Boruta all-relevant feature selection method.
Use Cases
Best For
- Identifying all relevant features in high-dimensional datasets for better model interpretation
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