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tf–idf (term frequency–inverse document frequency), is a numerical statistic using in nlp that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. tf–idf increases proportionally the number of times a word appears in the document.

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Why is the result of CountVectorizer * TfidfVectorizer.idf_ different from TfidfVectorizer.f...

CountVectorizer, TfidfVectorizer vocab_docs = set(chain(*[i.split() for i in df['docs'].unique()])) cv_docs = CountVectorizer(vocabulary=vocab_docs) cv_docs_s = cv_docs.fit_transform(df['docs']) I do TFIDF … : tfidf_docs = TfidfVectorizer(vocabulary=vocab_docs) tfidf_docs_s = tfidf_docs.fit_transform(df['docs']) # tfidf docs tfidf_docs_s = tfidf_docs_s.todense() but I see that the results are different: …