面对ValueError：目标是多类但平均='二进制'

Question

我是 python 和机器学习的新手。根据我的要求，我正在尝试对我的数据集使用朴素贝叶斯算法。

我能够找出准确度，但试图找出准确度和召回率。但是，它抛出以下错误：

   "choose another average setting." % y_type)
ValueError: Target is multiclass but average='binary'. Please choose another average setting.

谁能建议我如何继续它。我尝试在精度和召回分数中使用 average ='micro'。它没有任何错误，但它给出的准确度、精度、召回分数相同。

我的数据集：

train_data.csv：

review,label
Colors & clarity is superb,positive
Sadly the picture is not nearly as clear or bright as my 40 inch Samsung,negative

test_data.csv：

review,label
The picture is clear and beautiful,positive
Picture is not clear,negative

我的代码：

from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
from sklearn.metrics import confusion_matrix
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score


def load_data(filename):
    reviews = list()
    labels = list()
    with open(filename) as file:
        file.readline()
        for line in file:
            line = line.strip().split(',')
            labels.append(line[1])
            reviews.append(line[0])

    return reviews, labels

X_train, y_train = load_data('/Users/abc/Sep_10/train_data.csv')
X_test, y_test = load_data('/Users/abc/Sep_10/test_data.csv')

vec = CountVectorizer() 

X_train_transformed =  vec.fit_transform(X_train) 

X_test_transformed = vec.transform(X_test)

clf= MultinomialNB()
clf.fit(X_train_transformed, y_train)

score = clf.score(X_test_transformed, y_test)
print("score of Naive Bayes algo is :" , score)

y_pred = clf.predict(X_test_transformed)
print(confusion_matrix(y_test,y_pred))

print("Precision Score : ",precision_score(y_test,y_pred,pos_label='positive'))
print("Recall Score :" , recall_score(y_test, y_pred, pos_label='positive') )

Answer 1

您需要添加'average' 参数。根据the documentation：

average : string, [None, ‘binary’ (默认), ‘micro’, ‘macro’, “样本”、“加权”]

多类/多标签目标需要此参数。如果None，则 返回每个班级的分数。否则，这 确定对数据执行的平均类型：

这样做：

print("Precision Score : ",precision_score(y_test, y_pred, 
                                           pos_label='positive'
                                           average='micro'))
print("Recall Score : ",recall_score(y_test, y_pred, 
                                           pos_label='positive'
                                           average='micro'))

将'micro' 替换为除'binary' 之外的任一上述选项。此外，在多类设置中，无需提供'pos_label'，因为它无论如何都会被忽略。

评论更新：

是的，它们可以相等。它在user guide here 中给出：

请注意，对于多类设置中的“微”平均，所有 包含的标签将产生相等的精度、召回率和 F，而 “加权”平均可能会产生不介于两者之间的 F 分数 准确率和召回率。