>>> from sklearn.model_selection import RandomizedSearchCV
>>> pipe_svc = make_pipeline(StandardScaler(),
...                          SVC(random_state=1))
>>> param_grid = [{'svc__C': param_range,
...                'svc__kernel': ['linear']},
...               {'svc__C': param_range,
...                'svc__gamma': param_range,
...                'svc__kernel': ['rbf']}]
>>> rs = RandomizedSearchCV(estimator=pipe_svc,
...                         param_distributions=param_grid,
...                         scoring='accuracy',
...                         refit=True,
...                         n_iter=20,
...                         cv=10,
...                         random_state=1,
...                         n_jobs=-1)
>>> rs = rs.fit(X_train, y_train)
>>> print(rs.best_score_)
0.9670531400966184
>>> print(rs.best_params_)
{'svc__C': 0.05971247755848464, 'svc__kernel': 'linear'}