evalml.pipelines.LogisticRegressionPipeline

class evalml.pipelines.LogisticRegressionPipeline(parameters, objective, random_state=0)

Logistic Regression Pipeline for both binary and multiclass classification

name = 'Logistic Regression Pipeline'

summary = 'Logistic Regression Classifier w/ One Hot Encoder + Simple Imputer + Standard Scaler'

component_graph = ['One Hot Encoder', 'Simple Imputer', 'Standard Scaler', <evalml.pipelines.components.estimators.classifiers.logistic_regression.LogisticRegressionClassifier object>]

supported_problem_types = ['binary', 'multiclass']

model_family = 'linear_model'

hyperparameters = {'C': Real(low=0.01, high=10, prior='uniform', transform='identity'), 'impute_strategy': ['mean', 'median', 'most_frequent'], 'penalty': ['l2']}

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
feature_importance_graph	Generate a bar graph of the pipeline’s feature importances
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
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 current and additional objectives