Pandas Python Sklearn“；ValueError：分类指标可以'；t处理多类多输出和二进制目标的混合”；错误

当我使用test\u train\u split函数来使用相同的数据集进行测试和训练时，我没有遇到这个错误。 但是当我尝试使用不同的csv文件进行测试和培训时，我会遇到这个错误。 有人能解释一下我为什么会犯这个错误吗

from sklearn.linear_model import LogisticRegression
logreg=LogisticRegression()
logreg.fit(df,survived_df)
predictions=logreg.predict(test)

from sklearn.metrics import  accuracy_score
accuracy=accuracy_score(test_survived,predictions)   #error here Value Error ""ValueError: Classification metrics can't handle a mix of multiclass-multioutput and binary targets""
print(accuracy)

完全错误

ValueError                                Traceback (most recent call last)

<ipython-input-243-89c8ae1a928d> in <module>
----> 1 logreg.score(test,test_survived)
      2 

~/mldl/kaggle_practice/titanic_pilot/venv/lib64/python3.8/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
    497         """
    498         from .metrics import accuracy_score
--> 499         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
    500 
    501     def _more_tags(self):

~/mldl/kaggle_practice/titanic_pilot/venv/lib64/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~/mldl/kaggle_practice/titanic_pilot/venv/lib64/python3.8/site-packages/sklearn/metrics/_classification.py in accuracy_score(y_true, y_pred, normalize, sample_weight)
    185 
    186     # Compute accuracy for each possible representation
--> 187     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
    188     check_consistent_length(y_true, y_pred, sample_weight)
    189     if y_type.startswith('multilabel'):

~/mldl/kaggle_practice/titanic_pilot/venv/lib64/python3.8/site-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
     88 
     89     if len(y_type) > 1:
---> 90         raise ValueError("Classification metrics can't handle a mix of {0} "
     91                          "and {1} targets".format(type_true, type_pred))
     92 

ValueError: Classification metrics can't handle a mix of multiclass-multioutput and binary targets

---

您可能希望获得

预测

的准确性以及

测试_surved

数据帧的

surved

列：

from sklearn.metrics import  accuracy_score
accuracy=accuracy_score(test_survived['Survived'],predictions)
print(accuracy)

发生错误，因为精度_score（）只接受两个一维数组，一个作为基本真理标签，另一个作为预测标签。但是您提供了一个二维“数组”（数据帧）和一维预测，因此它假设您的第一个输入是一个多类输出

政府在这方面也很有办法

from sklearn.metrics import  accuracy_score
accuracy=accuracy_score(test_survived['Survived'],predictions)
print(accuracy)