I am a student who is studying machine running alone. As far as I know, the Tf-idf Vectorizer is a function that combines the Count Vectorizer and the Tf-idf Transformer. I was wondering if the Tf-idf Transformer process could actually have a impact on the model learning process. In my experience, there seemed to be no model performance difference between using CountVectorizer and Tf-idf Vectorizer in preprocessing String vector. Is there a situation where we need to consider the Tf-idfVectorizer rather than the CountVectorizer when processing a String Vector?