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"""
This module sets up a Random Forest Regressor with hyperparameter tuning.
Features:
- Uses `RandomForestRegressor` estimator from scikit-learn.
- Defines a hyperparameter grid for ensemble parameters.
- Handles non-linear relationships and reduces overfitting through averaging.
Special Considerations:
- Random Forests are robust to outliers and can handle non-linear data.
- Not sensitive to feature scaling.
- Set `n_jobs=-1` to utilize all available CPU cores.
"""
from sklearn.ensemble import RandomForestRegressor
# Define the estimator
estimator = RandomForestRegressor(random_state=42, n_jobs=-1)
# Define the hyperparameter grid
param_grid = {
'model__n_estimators': [100, 200], # Focus on a small range of estimators
'model__max_depth': [10, 20, None], # Commonly used depth variations
'model__min_samples_split': [2, 5], # Commonly used split values
'model__min_samples_leaf': [1, 2], # Focused leaf size
'model__max_features': ['sqrt'], # "sqrt" is often optimal for Random Forests
'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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