---
Added
- **New Method: `rfe_brute_force`**
- Implemented the `rfe_brute_force` method for exhaustive feature elimination.
- This method iteratively adds features based on Recursive Feature Elimination (RFE) and evaluates model performance to find the best feature subset.
- Example usage:
python
best_features = fs.rfe_brute_force(estimator=RandomForestClassifier(), n_features_to_select=5, force=True)
print("Best Features:", best_features)
Changed
- **Backward Elimination and Forward Selection Enhancements**
- Added a `fast` parameter to both `backward_elimination` and `forward_selection` methods.
- When `fast=True`, these methods use a quicker evaluation process, reducing computational time.
- Example usage:
python
selected_features = fs.backward_elimination(significance_level=0.05, fast=True)
print("Selected Features (Fast Backward Elimination):", selected_features)
- Example usage for forward selection:
python
selected_features = fs.forward_selection(significance_level=0.05, fast=True)
print("Selected Features (Fast Forward Selection):", selected_features)
Fixed
- **Filter Method Improvements**
- Improved the `filter_method` to raise appropriate errors when using `chi2` for regression problems or `anova` for classification problems.
- Ensured better handling of data types and target variable compatibility.