TF-IDF vectorizer doesn't work better than countvectorizer (sci-kit learn
The question is, why not ? Both are different solutions.
What is your dataset, how many words, how are they labelled, how do you extract your features ?
countvectorizer simply count the words, if it does a good job, so be it.
I am working on a multilabel text classification problem with 10 labels. 
The dataset is small, +- 7000 items and +-7500 labels in total. I am using python sci-kit learn and something strange came up in the results. As a baseline I started out with using the countvectorizer and was actually planning on using the tfidf vectorizer which I thought would work better. But it doesn't.. with the countvectorizer I get a performance of a 0,1 higher f1score. (0,76 vs 0,65)
I cannot wrap my head around why this could be the case? 
There are 10 categories and one is called miscellaneous. Especially this one gets a much lower performance with tfidf. 
Does anyone know when tfidf could perform worse than count?