ValueError：“ mean_squared_error”不是有效的评分值

如何解决ValueError：“ mean_squared_error”不是有效的评分值

因此，我一直在从事我的第一个ML项目，并且作为其中一部分，我正在尝试从sci-kit学习中使用各种模型，并且我为随机森林模型编写了这段代码：

#Random Forest
reg = RandomForestRegressor(random_state=0,criterion = 'mse')
#Apply grid search for best parameters
params = {'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_err
results_test['random_forest'] = ts_err

但是，当我运行此代码时，出现以下错误：

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,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,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,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scoring='mean_squared_error'中删除GridSearchCV(pipe,cv=5)来运行它。当我这样做时，代码可以完美运行，并给出足够好的训练和测试错误。

无论如何，我不知道为什么在scoring='mean_squared_error'函数中使用GridSearchCV参数会引发该错误。我在做什么错了？

解决方法

根据documentation：

所有计分器对象均遵循以下约定：较高的返回值比较低的返回值更好。因此，用于度量模型与数据之间距离的度量（如metrics.mean_squared_error）可以作为 neg_mean_squared_error 获得，该度量返回度量的取反值。

这意味着您必须通过scoring='neg_mean_squared_error'才能使用均方误差评估网格搜索结果。