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from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
parameters = {'solver':['lbfgs', 'liblinear'], 'C' : [1, 20]}
model = LogisticRegression()
clf = GridSearchCV(model, parameters)
clf.fit(X_train, y_train)
best_model = clf.best_estimator_
score = best_model.score(X_train, y_train)
print("score : %0.3f" % score)