Source code for evalml.pipelines.classification.logistic_regression

from skopt.space import Real

from evalml.model_types import ModelTypes
from evalml.pipelines import PipelineBase
from evalml.pipelines.components import (
    LogisticRegressionClassifier,
    OneHotEncoder,
    SimpleImputer,
    StandardScaler
)
from evalml.problem_types import ProblemTypes


[docs]class LogisticRegressionPipeline(PipelineBase): """Logistic Regression Pipeline for both binary and multiclass classification""" name = "Logistic Regression Classifier w/ One Hot Encoder + Simple Imputer + Standard Scaler" model_type = ModelTypes.LINEAR_MODEL problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS] hyperparameters = { "penalty": ["l2"], "C": Real(.01, 10), "impute_strategy": ["mean", "median", "most_frequent"], }
[docs] def __init__(self, objective, penalty, C, impute_strategy, number_features, n_jobs=-1, random_state=0): imputer = SimpleImputer(impute_strategy=impute_strategy) enc = OneHotEncoder() scaler = StandardScaler() estimator = LogisticRegressionClassifier(random_state=random_state, penalty=penalty, C=C, n_jobs=n_jobs) super().__init__(objective=objective, component_list=[enc, imputer, scaler, estimator], n_jobs=n_jobs, random_state=random_state)