What's exactly ntc form in tf-idf vector space?
I suggest the sklearn.feature_extraction.text.TfidfVectorizer,
scikit learn is a bib in python used for training machine learning model,
it is easy and very useful,
I have a collection of text documents. I've been asked to show each document in tf-idf vector space and in ntc form and then, train a svm model based on documents' vectors in python. What does ntc exactly mean here?
I Found that it's the same as tf-idf weights with one step of normalization which is called "cosine normalization". But i can't find information about such thing. I found "cosine similarity" which is in my idea different from "cosine normalization". Are they the same? And how can i create this vector in python?