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"""
This module sets up an ElasticNet Regression model with hyperparameter tuning.
Features:
- Uses `ElasticNet` estimator from scikit-learn.
- Combines L1 and L2 regularization.
- Increases `max_iter` to address convergence warnings.
Special Considerations:
- May produce convergence warnings if `max_iter` is insufficient.
- Adjust `l1_ratio` to balance between Lasso and Ridge penalties.
- Applying a log transformation (`log_transform`) to the target variable can be beneficial if it's skewed.
- Ensure `OneHotEncoder` outputs dense arrays.
"""
from sklearn.linear_model import ElasticNet
# Define the estimator
estimator = ElasticNet()
# Define the hyperparameter grid
param_grid = {
'model__alpha': [0.01, 0.1, 1.0, 10.0], # Regularization strength
'model__l1_ratio': [0.2, 0.5, 0.8], # Balance between L1 (Lasso) and L2 (Ridge)
'model__max_iter': [5000], # Sufficient to avoid convergence warnings
'model__fit_intercept': [True], # Assume intercept is important
'model__selection': ['cyclic'], # Focus on the default cyclic selection
'preprocessor__num__imputer__strategy': ['mean'], # Single imputation strategy
'preprocessor__num__scaler__with_mean': [True], # StandardScaler
'preprocessor__num__scaler__with_std': [True], # StandardScaler
}
# Optional: Define the default scoring metric
default_scoring = 'neg_root_mean_squared_error'
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