evalml.pipelines.LogisticRegressionMulticlassPipeline

Inheritance diagram of LogisticRegressionMulticlassPipeline
class evalml.pipelines.LogisticRegressionMulticlassPipeline(parameters, random_state=0)[source]

Logistic Regression Pipeline for multiclass classification.

name = 'Logistic Regression Multiclass Pipeline'
custom_name = None
summary = 'Logistic Regression Classifier w/ One Hot Encoder + Simple Imputer + Standard Scaler'
component_graph = ['One Hot Encoder', 'Simple Imputer', 'Standard Scaler', 'Logistic Regression Classifier']
problem_type = 'multiclass'
model_family = 'linear_model'
hyperparameters = {'Logistic Regression Classifier': {'C': Real(low=0.01, high=10, prior='uniform', transform='identity'), 'penalty': ['l2']}, 'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}, 'Standard Scaler': {}}
custom_hyperparameters = None
default_parameters = {'Logistic Regression Classifier': {'C': 1.0, 'n_jobs': -1, 'penalty': 'l2'}, '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

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