LGBMHyperParams#
- class openstef_models.models.forecasting.lgbm_forecaster.LGBMHyperParams(**data: Any) None[source]
Bases:
HyperParamsLightGBM hyperparameters for gradient boosting tree models.
Example
Creating custom hyperparameters for deep trees with regularization
>>> hyperparams = LGBMHyperParams( ... n_estimators=200, ... max_depth=8, ... learning_rate=0.1, ... reg_alpha=0.1, ... reg_lambda=1.0, ... )
Note
These parameters are optimized for probabilistic forecasting with quantile regression. The default objective function is specialized for magnitude-weighted pinball loss.
- Parameters:
data (
Any)
-
n_estimators:
int
-
learning_rate:
float
-
max_depth:
int
-
min_child_weight:
float
-
min_data_in_leaf:
int
-
min_data_in_bin:
int
-
reg_alpha:
float
-
reg_lambda:
float
-
num_leaves:
int
-
max_bin:
int
-
colsample_bytree:
float
- classmethod forecaster_class() type[LGBMForecaster][source]
Create a LightGBM forecaster instance from this configuration.
- Return type:
type[LGBMForecaster]- Returns:
Forecaster class associated with this configuration.
- model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].