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/ Simple Imputer + One Hot Encoder'

component_graph = ['Simple Imputer', 'One Hot Encoder', 'XGBoost Classifier']

problem_type = 'binary'

model_family = 'xgboost'

hyperparameters = {'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}, 'XGBoost Classifier': {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=1, high=10, 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')}}

custom_hyperparameters = None

default_parameters = {'One Hot Encoder': {'categories': None, 'drop': None, 'handle_missing': 'error', 'handle_unknown': 'ignore', 'top_n': 10}, 'Simple Imputer': {'fill_value': None, 'impute_strategy': 'most_frequent'}, 'XGBoost Classifier': {'eta': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100}}

Instance attributes

feature_importance	Return importance associated with each feature.
parameters	Returns parameter dictionary for this pipeline
threshold

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
clone	Constructs a new pipeline with the same parameters and components.
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 importance
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