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"""
This module sets up an Extra Trees Regressor with hyperparameter tuning.
Features:
- Uses `ExtraTreesRegressor` estimator from scikit-learn.
- Defines a hyperparameter grid for ensemble parameters.
- Similar to Random Forest but uses random thresholds for splitting.
Special Considerations:
- Not sensitive to feature scaling.
- Can handle large datasets efficiently.
- Less prone to overfitting compared to single decision trees.
"""
from sklearn.ensemble import ExtraTreesRegressor
# Define the estimator
estimator = ExtraTreesRegressor(random_state=42, n_jobs=-1)
# Define the hyperparameter grid
param_grid = {
'model__n_estimators': [100, 200], # Common range for estimators
'model__criterion': ['squared_error'], # Focus on the most widely used criterion
'model__max_depth': [None, 10, 20], # Unrestricted depth and reasonable constraints
'model__min_samples_split': [2, 5], # Commonly used values
'model__min_samples_leaf': [1, 2], # Prevent overfitting with larger leaves
'model__max_features': ['sqrt', 'log2'], # Reduce to most common feature sampling strategies
'preprocessor__num__imputer__strategy': ['mean'], # Single imputation strategy
}
# Optional: Define the default scoring metric
default_scoring = 'neg_root_mean_squared_error'
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