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

我能够找出准确度，但尝试找出准确度并回想一下。但是，它引发以下错误：

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

谁能建议我如何进行。我曾尝试在平均值和召回率分数中使用average
=’micro’。它的工作原理没有任何错误，但在准确性，准确性和召回率上却给出了相同的分数。

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') )