evalml.pipelines.XGBoostBinaryPipeline

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

XGBoost Pipeline for binary classification

name = 'XGBoost Binary Classification Pipeline'

custom_name = 'XGBoost Binary 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', 'XGBoost Classifier']

problem_type = 'binary'

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
threshold

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.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on objectives