evalml.pipelines.ENBinaryPipeline

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

Elastic Net Pipeline for binary classification problems.

name = 'ENBinary Pipeline'

custom_name = None

summary = 'Elastic Net Classifier w/ One Hot Encoder + Simple Imputer'

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

problem_type = 'binary'

model_family = 'linear_model'

hyperparameters = {'Elastic Net Classifier': {'alpha': Real(low=0, high=1, prior='uniform', transform='identity'), 'l1_ratio': Real(low=0, high=1, prior='uniform', transform='identity')}, 'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}}

custom_hyperparameters = None

default_parameters = {'Elastic Net Classifier': {'alpha': 0.5, 'l1_ratio': 0.5, 'max_iter': 1000, 'n_jobs': -1}, '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'}}

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