Tune hyperparameters using grid search CV on logit

logistic_regression.py

cv_params = {'C': [0.001, 0.01, 0.1, 1., 10., 100.], 
             'penalty': ['l1', 'l2'],
             'class_weight': [None, 'balanced']
            }   
fix_params = {'random_state': SEED}
log_cv_1 = GridSearchCV(LogisticRegression(**fix_params), cv_params, scoring='f1', cv=5)
log_cv_1.fit(X_train, y_train)
log_clf_all = LogisticRegression(**{**fix_params, **log_cv_1.best_params_})
_ = myUtilityFunction.prediction_evaluation(log_clf_all, X_train, X_test, y_train, y_test, 
                                            X_train.columns, "coefficients")