我创建了一个朴素贝叶斯模型来预测结果是“负面”还是“正面”。我遇到的问题是在一组新的数据上运行模型时，其中一些词不在模型中。我在预测新数据集时收到的错误是：

ValueError: Expected input with 6 features, got 4 instead

我读到我需要在模型中加入拉普拉斯平滑器，而Bernoulli()已经有一个默认的alpha值为1。我还能做些什么来修复我的错误？谢谢

from nltk.corpus import stopwordsfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.feature_extraction.text import TfidfTransformerfrom sklearn.naive_bayes import BernoulliNBfrom sklearn import cross_validationfrom sklearn.metrics import classification_reportimport numpy as npfrom sklearn.metrics import accuracy_scoreimport textblob as TextBlob#scikitcomments = list(['happy','sad','this is negative','this is positive', 'i like this', 'why do i hate this'])classes = list(['positive','negative','negative','positive','positive','negative'])# preprocess creates the term frequency matrix for the review data setstop = stopwords.words('english')count_vectorizer = CountVectorizer(analyzer =u'word',stop_words = stop, ngram_range=(1, 3))comments = count_vectorizer.fit_transform(comments)tfidf_comments = TfidfTransformer(use_idf=True).fit_transform(comments)# preparing data for split validation. 60% training, 40% testdata_train,data_test,target_train,target_test = cross_validation.train_test_split(tfidf_comments,classes,test_size=0.2,random_state=43)classifier = BernoulliNB().fit(data_train,target_train)#new datacomments_new = list(['positive','zebra','george','nothing'])comments_new = count_vectorizer.fit_transform(comments_new)tfidf_comments_new = TfidfTransformer(use_idf=True).fit_transform(comments_new)classifier.predict(tfidf_comments_new)

回答：

你不应该在新数据上使用fit_transform来拟合一个新的估计器，而应该使用之前构建的count_vectorizer，只使用transform。这样会忽略所有不在字典中的词。

我不同意Maxim的观点：虽然这对CountVectorizer没有影响，但在联合数据集上使用TfidfTransformer会将测试集的信息泄露到训练集中，这是你需要避免的。