evalml.pipelines.ETBinaryClassificationPipeline

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

Extra Trees Pipeline for binary classification

name = 'Extra Trees Binary Classification Pipeline'

custom_name = 'Extra Trees Binary Classification Pipeline'

summary = 'Extra Trees Classifier w/ One Hot Encoder + Simple Imputer'

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

problem_type = 'binary'

model_family = 'extra_trees'

hyperparameters = {'Extra Trees Classifier': {'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}, 'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}}

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