通过GridSearchCV对CatBoostClassifier进行参数调优

    def print_best_score(gsearch,param_test):
     # 输出best score
    print("Best score: %0.3f" % gsearch.best_score_)
    print("Best parameters set:")
    # 输出最佳的分类器到底使用了怎样的参数
    best_parameters = gsearch.best_estimator_.get_params()
    for param_name in sorted(param_test.keys()):
        print("\t%s: %r" % (param_name, best_parameters[param_name]))
    
    params = {'depth': [4, 6, 10],
          'learning_rate' : [0.05, 0.1, 0.15],
    #          'l2_leaf_reg': [1,4,9]
    #          'iterations': [1200],
    #           'early_stopping_rounds':[1000],
    #           'task_typ