The context of the question:
I have a pandas dataframe where one column has text values and others have categorical values. I trained a
word2vec model with tensorflow with some sample data. And I convert my text column into the vector representation. But I want to feed these data to Catboost for regression task. But I can't feed those data to catboost because of catboost only accept the
I found a tutorial on Catboost Github page for this. But this example is for the classification task. It finds
cosineand other types of relationship between two vector representation of text. But in my case, I have only one text field. So how can calculate
cosineor other types relationship?
So my question is how to extract categorical features from the vector representation of text data?