Logrec_Hyperparametertuning.py

## Base Model
logrec = LogisticRegression()

## Parameter Grid
param_grid = [    
    {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],
    'C' : np.logspace(-4, 4, 20),
    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],
    'max_iter' : [100, 800,1000, 1200]
    }
]

### Hyper parameter Tuning
from sklearn.model_selection import GridSearchCV

clf = GridSearchCV(logModel, param_grid = param_grid, cv = 3, verbose=True)
gsc = clf.fit(X,y)

print(gsc.best_estimator_)
----------------------------------------
LogisticRegression(C=6.15848, class_weight=None, dual=False,
                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,
                   max_iter=800, multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,
                   warm_start=False)