from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(rfc,parameters,cv=5)
cv.fit(train_features,train_label.values.ravel())display(cv)def display(results):
print(f'Best parameters are: {results.best_params_}')
print("\n")
mean_score = results.cv_results_['mean_test_score']
std_score = results.cv_results_['std_test_score']
params = results.cv_results_['params']
for mean,std,params in zip(mean_score,std_score,params):
print(f'{round(mean,3)} + or -{round(std,3)} for the {params}')