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"""
This module sets up a LightGBM Regressor with hyperparameter tuning.
Features:
- Uses `LGBMRegressor` estimator from LightGBM.
- Defines a hyperparameter grid for boosting parameters.
- Optimized for speed and performance.
Special Considerations:
- Requires the `lightgbm` library (`pip install lightgbm`).
- Can handle categorical features if provided appropriately.
- Not sensitive to feature scaling.
"""
from lightgbm import LGBMRegressor
# Define the estimator
estimator = LGBMRegressor(
random_state=42,
n_jobs=-1,
verbose=-1
)
# Define hyperparameter grid
param_grid = {
'model__n_estimators': [100, 200],
'model__learning_rate': [0.01, 0.05],
'model__num_leaves': [15, 31],
'model__max_depth': [10, 20],
'model__min_data_in_leaf': [20, 50],
'model__colsample_bytree': [0.8],
'preprocessor__num__imputer__strategy': ['mean'],
}
# Optional: Define the default scoring metric
default_scoring = 'neg_root_mean_squared_error'
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