catboost#

Source code: sensai/catboost.py

class CatBoostVectorRegressionModel(categorical_feature_names: Optional[Union[Sequence[str], str]] = None, random_state=42, num_leaves=31, **model_args)[source]#

Bases: AbstractSkLearnMultipleOneDimVectorRegressionModel

Parameters:

  • categorical_feature_names – sequence of feature names in the input data that are categorical. Columns that have dtype ‘category’ (as will be the case for categorical columns created via FeatureGenerators) need not be specified (should be inferred automatically). In general, passing categorical features is preferable to using one-hot encoding, for example.

  • random_state – the random seed to use

  • num_leaves – the maximum number of leaves in one tree (original catboost default is 31)

  • model_args – see https://catboost.ai/docs/concepts/python-reference_parameters-list.html#python-reference_parameters-list

log = <Logger sensai.catboost.CatBoostVectorRegressionModel (WARNING)>#
class CatBoostVectorClassificationModel(categorical_feature_names: Optional[Sequence[str]] = None, random_state=42, num_leaves=31, **model_args)[source]#

Bases: AbstractSkLearnVectorClassificationModel

Parameters:

  • categorical_feature_names – sequence of feature names in the input data that are categorical Columns that have dtype ‘category’ (as will be the case for categorical columns created via FeatureGenerators) need not be specified (should be inferred automatically, but we have never actually tested this behaviour successfully for a classification model). In general, passing categorical features may be preferable to using one-hot encoding, for example.

  • random_state – the random seed to use

  • num_leaves – the maximum number of leaves in one tree (original catboost default is 31)

  • model_args – see https://catboost.ai/docs/concepts/python-reference_parameters-list.html#python-reference_parameters-list

log = <Logger sensai.catboost.CatBoostVectorClassificationModel (WARNING)>#