问TF-IDF向量可以在不同级别的输入标记(单词、字符、n-gram)中生成，我们应该使用哪种？

EN

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-grams
tfidf_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 dataset
tfidf_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)