Python ValueError:error_score必须是字符串'；提高'；或一个数值。（提示：如果使用'；raise'；，请确保拼写正确。）

我正在用Python进行决策树回归

出现了一个奇怪的错误：

ValueError: error_score must be the string 'raise' or a numeric value. (Hint: if using 'raise', please make sure that it has been spelled correctly.)

我的数据框大约有330000*91列

我的代码如下所示：

# Read data
y = my_train2.iloc[:,0]
x = my_train2.iloc[:, 1:93]

(x_train, x_test, y_train, y_test) = cv.train_test_split(x, y, test_size=.20)
# Instantiate dt
dt = DecisionTreeRegressor(# max_depth = 8,
                       min_samples_leaf = 1,
                       random_state = 1)

# Fit dt to the training set
dt.fit(x_train, y_train)

下面的块给出了一个错误

from sklearn.model_selection import cross_val_score
# Compute the array containing the 10-folds CV MSEs
MSE_CV_scores = - cross_val_score(dt, x_train, y_train, cv=10, 
                              scoring='neg_mean_squared_error', 
                              n_jobs=-1) 

# Compute the 10-folds CV RMSE
RMSE_CV = (MSE_CV_scores.mean())**(1/2)

# Print RMSE_CV
print('CV RMSE: {:.2f}'.format(RMSE_CV))

如果需要，我可以在这里打印每列的类

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谢谢

可能与您的scikit learn版本有关。我猜这个错误出现在0.22。大概将其降级至0.21并重试

可能与您的scikit learn版本有关。我猜这个错误出现在0.22。大概将其降级为0.21，然后重试