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"""
This module sets up an XGBoost Regressor with hyperparameter tuning.
Features:
- Uses `XGBRegressor` estimator from XGBoost.
- Defines a hyperparameter grid for boosting parameters.
- Efficient and scalable implementation of gradient boosting.
Special Considerations:
- Requires the `xgboost` library (`pip install xgboost`).
- Handles missing values internally.
- Not sensitive to feature scaling.
- May require setting `tree_method` to 'gpu_hist' for GPU acceleration if available.
"""
from xgboost import XGBRegressor
# Define the estimator
estimator = XGBRegressor(random_state=42, n_jobs=-1, verbosity=0)
# Define the hyperparameter grid
param_grid = {
'model__n_estimators': [100, 200], # Common range for estimators
'model__learning_rate': [0.05, 0.1], # Common learning rates
'model__max_depth': [3, 5], # Typical depth for gradient boosting
'model__subsample': [0.8], # Fixed subsample value to reduce complexity
'model__colsample_bytree': [0.8], # Fixed colsample value to reduce complexity
'model__reg_alpha': [0, 0.1], # Focus on smaller values for L1 regularization
'model__reg_lambda': [1], # Default L2 regularization
'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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