如何修复 ValueError：分类指标无法处理模型的多类和多标签指标目标的混合？

Question

我创建了一个多类分类模型，其中输出变量有 6 个类。当我尝试获取准确度分数时出现错误。我尝试过其他答案，但答案没有帮助。

代码

#Converting Target Variable to Numeric

lang = {'US':1, 'UK':2, 'GE':3, 'IT':4, 'FR':5, 'ES':6} 

df.language = [lang[item] for item in df.language] 

#Creating Input Features and Target Variables

X= df.iloc[:,1:13]

y= df.iloc[:,0]

#Standardizing the Input Features

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

X = scaler.fit_transform(X)

#Train Test Split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

#Model

model = Sequential()

model.add(Dense(12, activation='relu', kernel_initializer='random_normal', input_dim=12))

model.add(Dense(10, activation='relu', kernel_initializer='random_normal'))

model.add(Dense(8, activation='relu', kernel_initializer='random_normal'))

#Output Layer

model.add(Dense(7, activation = 'softmax', kernel_initializer='random_normal'))

#Compiling the neural network

model.compile(optimizer ='adam',loss='sparse_categorical_crossentropy', metrics =['accuracy'])

#Fitting the data to the training dataset

model.fit(X_train,y_train, batch_size=5, epochs=100)

#Make predictions

pred_train = model.predict(X_train)

pred_test = model.predict(X_test)  

   

print('Train Accuracy = ',accuracy_score(y_train,pred_train.round()))

print('Test Accuracy = ',accuracy_score(y_test,pred_test.round()))

错误

ValueError: Classification metrics can't handle a mix of multiclass and multilabel-indicator targets

变量保存的值 我正在添加所需变量保存的值。我相信我收到的输出变量的数量不正确，因为 1 个值有多个输出。

Answer 1

predict返回样本属于每个类的概率，但accuracy_score需要类标签。您必须从预测中获取类别标签。使用accuracy_score(y, np.argmax(pred_train, axis=1))np.argmax返回概率最高的类的标签，因为您对一批数据而不是单个样本进行了预测，所以您必须使用axis=1。