evalml.pipelines.components.XGBoostClassifier

class evalml.pipelines.components.XGBoostClassifier(eta=0.1, max_depth=3, min_child_weight=1, n_estimators=100, random_state=0)

XGBoost Classifier

name = 'XGBoost Classifier'

model_family = 'xgboost'

supported_problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.MULTICLASS: 'multiclass'>]

hyperparameter_ranges = {'eta': Real(low=0, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=1, high=20, prior='uniform', transform='identity'), 'min_child_weight': Real(low=1, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=1, high=1000, prior='uniform', transform='identity')}

Instance attributes

feature_importances

Returns feature importances.

Methods:

__init__	Initialize self.
describe	Describe a component and its parameters
fit	Fits component to data
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.