evalml.pipelines.XGBoostRegressionPipeline

class evalml.pipelines.XGBoostRegressionPipeline(parameters, random_state=0)

XGBoost Pipeline for regression problems

name = 'XGBoost Regression Pipeline'

custom_name = None

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

component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Regressor Select From Model', 'XGBoost Regressor']

problem_type = 'regression'

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
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
graph_feature_importance	Generate a bar graph of the pipeline’s feature importances
load	Loads pipeline at file path
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives