xgboost

Source code: sensai/xgboost.py

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is_xgboost_version_at_least(major: int, minor: Optional[int] = None, patch: Optional[int] = None)

class XGBGradientBoostedVectorRegressionModel(random_state=42, early_stopping_rounds: Optional[int] = None, early_stopping_data_splitter: Optional[DataSplitter] = None, **model_args)

Bases: AbstractSkLearnMultipleOneDimVectorRegressionModel, FeatureImportanceProviderSkLearnRegressionMultipleOneDim

XGBoost’s regression model using gradient boosted trees

Parameters:

model_args – See https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor

is_sample_weight_supported() → bool

class XGBRandomForestVectorRegressionModel(random_state=42, **model_args)

Bases: AbstractSkLearnMultipleOneDimVectorRegressionModel, FeatureImportanceProviderSkLearnRegressionMultipleOneDim

XGBoost’s random forest regression model

Parameters:

model_args – See https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRFRegressor

is_sample_weight_supported() → bool

class XGBGradientBoostedVectorClassificationModel(random_state=42, use_balanced_class_weights=False, **model_args)

Bases: AbstractSkLearnVectorClassificationModel, FeatureImportanceProviderSkLearnClassification

XGBoost’s classification model using gradient boosted trees

Parameters:

model_args – See https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier

is_sample_weight_supported() → bool

class XGBRandomForestVectorClassificationModel(random_state=42, use_balanced_class_weights=False, **model_args)

Bases: AbstractSkLearnVectorClassificationModel, FeatureImportanceProviderSkLearnClassification

XGBoost’s random forest classification model

Parameters:

model_args – See https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRFClassifier

is_sample_weight_supported() → bool