LogisticRegression: `'multi_class'` param

[d-kleine]

Describe the bug

With the upcoming sklearn version 1.7, the 'multi_class' param of LogisticRegression() will be deprecated

source: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

Steps/Code to Reproduce

from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS

lr = LogisticRegression(multi_class='multinomial', 
                        solver='newton-cg', 
                        random_state=123)

efs1 = EFS(estimator=lr, 
           min_features=2,
           max_features=3,
           scoring='accuracy',
           print_progress=False,
           clone_estimator=False,
           cv=5,
           n_jobs=1)

pipe = make_pipeline(efs1, lr)

param_grid = {'exhaustivefeatureselector__estimator__C': [0.1, 1.0, 10.0]}
    
gs = GridSearchCV(estimator=pipe, 
                  param_grid=param_grid, 
                  scoring='accuracy', 
                  n_jobs=1, 
                  cv=2, 
                  verbose=1, 
                  refit=False)

# run gridearch
gs = gs.fit(X_train, y_train)

Expected Results

no warning message

Actual Results

FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.

Versions

MLxtend 0.23.4
Windows-10-10.0.26100-SP0
Python 3.11.11 | packaged by Anaconda, Inc. | (main, Dec 11 2024, 16:34:19) [MSC v.1929 64 bit (AMD64)]
Scikit-learn 1.6.1
NumPy 2.2.4
SciPy 1.15.2