因此，我一直在进行我的第一个机器学习项目，作为其中的一部分，我尝试了来自sci-kit learn的各种模型，并且我为随机森林模型编写了如下代码：

#Random Forestreg = RandomForestRegressor(random_state=0, criterion = 'mse')#Apply grid search for best parametersparams = {'randomforestregressor__n_estimators' : range(100, 500, 200),          'randomforestregressor__min_samples_split' : range(2, 10, 3)}pipe = make_pipeline(reg)grid = GridSearchCV(pipe, param_grid = params, scoring='mean_squared_error', n_jobs=-1, iid=False, cv=5)reg = grid.fit(X_train, y_train)print('Best MSE: ', grid.best_score_)print('Best Parameters: ', grid.best_estimator_)y_train_pred = reg.predict(X_train)y_test_pred = reg.predict(X_test)tr_err = mean_squared_error(y_train_pred, y_train)ts_err = mean_squared_error(y_test_pred, y_test)print(tr_err, ts_err)results_train['random_forest'] = tr_errresults_test['random_forest'] = ts_err

但是，当我运行这段代码时，我得到了以下错误：

KeyError                                  Traceback (most recent call last)~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in get_scorer(scoring)    359             else:--> 360                 scorer = SCORERS[scoring]    361         except KeyError:KeyError: 'mean_squared_error'During handling of the above exception, another exception occurred:ValueError                                Traceback (most recent call last)<ipython-input-149-394cd9e0c273> in <module>      5 pipe = make_pipeline(reg)      6 grid = GridSearchCV(pipe, param_grid = params, scoring='mean_squared_error', n_jobs=-1, iid=False, cv=5)----> 7 reg = grid.fit(X_train, y_train)      8 print('Best MSE: ', grid.best_score_)      9 print('Best Parameters: ', grid.best_estimator_)~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)     71                           FutureWarning)     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})---> 73         return f(**kwargs)     74     return inner_f     75 ~\anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)    652         cv = check_cv(self.cv, y, classifier=is_classifier(estimator))    653 --> 654         scorers, self.multimetric_ = _check_multimetric_scoring(    655             self.estimator, scoring=self.scoring)    656 ~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in _check_multimetric_scoring(estimator, scoring)    473     if callable(scoring) or scoring is None or isinstance(scoring,    474                                                           str):--> 475         scorers = {"score": check_scoring(estimator, scoring=scoring)}    476         return scorers, False    477     else:~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)     71                           FutureWarning)     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})---> 73         return f(**kwargs)     74     return inner_f     75 ~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in check_scoring(estimator, scoring, allow_none)    403                         "'fit' method, %r was passed" % estimator)    404     if isinstance(scoring, str):--> 405         return get_scorer(scoring)    406     elif callable(scoring):    407         # Heuristic to ensure user has not passed a metric~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in get_scorer(scoring)    360                 scorer = SCORERS[scoring]    361         except KeyError:--> 362             raise ValueError('%r is not a valid scoring value. '    363                              'Use sorted(sklearn.metrics.SCORERS.keys()) '    364                              'to get valid options.' % scoring)ValueError: 'mean_squared_error' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

因此，我尝试通过从GridSearchCV(pipe, param_grid = params, scoring='mean_squared_error', n_jobs=-1, iid=False, cv=5)中移除scoring='mean_squared_error'来运行它。当我这样做时，代码运行得很好，并且给出了足够好的训练和测试误差。

尽管如此，我还是无法弄清楚为什么在GridSearchCV函数中使用scoring='mean_squared_error'参数会抛出那个错误。我做错了什么？

回答：

根据文档：

所有评分器对象都遵循这样的约定：返回值越高越好。因此，像metrics.mean_squared_error这样的度量模型与数据之间的距离的度量，可以作为neg_mean_squared_error可用，它返回度量的负值。

这意味着你必须传递scoring='neg_mean_squared_error'，以便使用均方误差来评估网格搜索结果。