Logistic Regression GridSearchCV

[ijyliu]

Perform hyperparameter tuning for logistic regression.

hyperparameter_settings = [
    # Non-penalized
    {'solver': ['newton-cg', 'lbfgs', 'sag', 'saga'], 
     'penalty': [None], 
     'C': [1],  # C is irrelevant here but required as a placeholder
     'class_weight': [None, 'balanced'], 
     'multi_class': ['ovr', 'multinomial']},
    # ElasticNet penalty
    {'solver': ['saga'], 
     'penalty': ['elasticnet'], 
     'C': [0.001, 0.01, 0.1, 1, 10, 100], 
     'l1_ratio': [0.0, 0.25, 0.5, 0.75, 1.0], 
     'class_weight': [None, 'balanced'], 
     'multi_class': ['ovr', 'multinomial']}
]

# Fit model
# Perform grid search with 5 fold cross validation
lr = LogisticRegression(max_iter=1000) # higher to encourage convergence
gs = GridSearchCV(lr, hyperparameter_settings, scoring='accuracy', cv=5, n_jobs=-1).fit(X, y)