evalml.pipelines.XGBoostPipeline

class evalml.pipelines.XGBoostPipeline(parameters, objective, random_state=0)

XGBoost Pipeline for both binary and multiclass classification

name = 'XGBoost Classification Pipeline'

summary = 'XGBoost Classifier w/ One Hot Encoder + Simple Imputer + RF Classifier Select From Model'

component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Classifier Select From Model', <evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier object>]

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

model_family = 'xgboost'

hyperparameters = {'eta': Real(low=0, high=1, prior='uniform', transform='identity'), 'impute_strategy': ['mean', 'median', 'most_frequent'], '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'), 'percent_features': Real(low=0.01, high=1, prior='uniform', transform='identity'), 'threshold': ['mean', -inf]}

custom_hyperparameters = None

Instance attributes

feature_importances	Return feature importances.
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
describe	Outputs pipeline details including component parameters
feature_importance_graph	Generate a bar graph of the pipeline’s feature importances
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
load	Loads pipeline at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives