我已经查看了这个回答，但没有理解。 当我使用test_train_split函数对同一数据集进行测试和训练时，我不会得到这个错误。但是当我尝试使用不同的csv文件进行测试和训练时，我会得到这个错误。泰坦尼克号Kaggle竞赛链接有人能解释一下我为什么会得到这个错误吗？

from sklearn.linear_model import LogisticRegressionlogreg=LogisticRegression()logreg.fit(df,survived_df)predictions=logreg.predict(test)from sklearn.metrics import  accuracy_scoreaccuracy=accuracy_score(test_survived,predictions)   #在这里报错 ValueError ""ValueError: Classification metrics can't handle a mix of multiclass-multioutput and binary targets""print(accuracy)

完整错误信息

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

完整代码

df=pd.read_csv('data/train.csv')test=pd.read_csv('data/test.csv')test_survived=pd.read_csv('data/gender_submission.csv')plt.figure(5)df=df.drop(columns=['Name','SibSp','Ticket','Cabin','Parch','Embarked'])test=test.drop(columns=['Name','SibSp','Ticket','Cabin','Parch','Embarked'])sns.heatmap(df.isnull(),),plt.figure(2)sns.boxplot(data=df,y='Age')# 从箱型图来看，第75百分位数似乎是38，第25百分位数似乎是20.....#所以在两端乘以1.5后，年龄范围(10,57)似乎不错，任何超出这个范围的值...我们将其视为异常值。#同时使用这个年龄范围来计算均值，以替换年龄的缺失值。df=df.loc[df['Age'].between(9,58),]# test=test.loc[test['Age'].between(9,58),]# test=test.loc[test['Age'].between(9,58),]df=df.reset_index(drop=True,)class_3_age=df.loc[df['Pclass']==3].Age.mean()class_2_age=df.loc[df['Pclass']==2].Age.mean()class_1_age=df.loc[df['Pclass']==1].Age.mean()def remove_null_age(data):    agee=data[0]    pclasss=data[1]    if pd.isnull(agee):        if pclasss==1:            return class_1_age        elif pclasss==2:            return class_2_age        else:            return  class_3_age    return ageedf['Age']=df[["Age","Pclass"]].apply(remove_null_age,axis=1)test['Age']=test[["Age","Pclass"]].apply(remove_null_age,axis=1)sex=pd.get_dummies(df['Sex'],drop_first=True)test_sex=pd.get_dummies(test['Sex'],drop_first=True)sex=sex.reset_index(drop=True)test_sex=test_sex.reset_index(drop=True)df=df.drop(columns=['Sex'])test=test.drop(columns=['Sex'])df=pd.concat([df,sex],axis=1)test=test.reset_index(drop=True)df=df.reset_index(drop=True)test=pd.concat([test,test_sex],axis=1)survived_df=df["Survived"]df=df.drop(columns='Survived')test["Age"]=test['Age'].round(1)test.at[152,'Fare']=30from sklearn.linear_model import LogisticRegressionlogreg=LogisticRegression()logreg.fit(df,survived_df)predictions=logreg.predict(test)from sklearn.metrics import  accuracy_scoreaccuracy=accuracy_score(test_survived,predictions)print(accuracy)

回答：

您可能希望获取predictions与test_survived数据框中Survived列的准确率：

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

您的错误发生是因为accuracy_score()只接受两个一维数组，一个作为真实标签，另一个作为预测标签。但您提供了一个二维的“数组”（数据框）和一维的预测，因此它假设您的第一个输入是多类输出。

文档对此也非常有帮助。