我已经阅读了很多博客，但对答案并不满意，假设我在几个文档示例上训练 tf-idf 模型：
" John like horror movie."
" Ryan watches dramatic movies"
------------so on ----------
我使用这个功能：
from sklearn.feature_extraction.text import TfidfTransformer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
print((X_train_counts.todense()))
# Gives count of words in each document
But it doesn't tell which word? How to get words as headers in X_train_counts
outputs. Similarly in X_train_tfidf ?
所以 X_train_tfidf 输出将是具有 tf-idf 分数的矩阵：
Horror watch movie drama
doc1 score1 -- -----------
doc2 ------------------------
这样对吗？
做什么fit和做什么transformation？在 sklearn …