a. 词级TF-IDF：矩阵表示不同文档中每个词项的tf-idf得分。

b. N-gram级TF-IDF：N-gram是N个词项的组合。该矩阵表示N-gram的tf-idf得分

c. 字符级TF-IDF：矩阵表示字符级的tf-idf得分

tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'\w{1,}', max_features=5000)tfidf_vect.fit(trainDF['texts'])xtrain_tfidf = tfidf_vect.transform(train_x)xvalid_tfidf = tfidf_vect.transform(valid_x)# ngram level tf-idf N-gram Level TF-IDF : N-grams are the combination of N terms together. This Matrix representing tf-idf scores of N-gramstfidf_vect_ngram = TfidfVectorizer(analyzer='word', token_pattern=r'\w{1,}', ngram_range=(2, 3), max_features=5000)tfidf_vect_ngram.fit(trainDF['texts'])xtrain_tfidf_ngram = tfidf_vect_ngram.transform(train_x)xvalid_tfidf_ngram = tfidf_vect_ngram.transform(valid_x)# characters level tf-idf Character Level TF-IDF : Matrix representing tf-idf scores of character level n-grams in the datasettfidf_vect_ngram_chars = TfidfVectorizer(analyzer='char', token_pattern=r'\w{1,}', ngram_range=(2, 3), max_features=5000)tfidf_vect_ngram_chars.fit(trainDF['texts'])xtrain_tfidf_ngram_chars = tfidf_vect_ngram_chars.transform(train_x)xvalid_tfidf_ngram_chars = tfidf_vect_ngram_chars.transform(valid_x)

回答：

没有一种方法适合所有情况。具体方法将取决于数据的性质。

你应该使用GridSearchCV来识别在你的具体情况下的最佳方法。这里有一个来自官方文档的文本特征提取流程的良好示例。