# SelectKBest for text analytics

I have a corpus of data

Class   Tweet
-1  toxic phenol ingredient in vaccines  the detrimental effects of injected phenol  have not been fully evaluated
-1  doctors give flu shots to pregnant women despite evidence of harm to fetus
-1   hearus autism and the mmr vaccine  the most diabolical medical scandal of the century       cdcwhistleblower  vaxxed
-1  rt   how to naturally detox from mandatory vaccine injections     hearus  cdcwhistleblower
-1  rt   the removal of vaccine exemptions forces parents who know that vaccines injure and sicken their children into coerced ha


I'll be doing some supervised learning on the text. I put the text through a pipeline as follows:

selector = SelectKBest(chi2, k = K)

clf = SVC(kernel = 'linear')

p = Pipeline([('vect', CountVectorizer()),
('tfidf',TfidfTransformer() ),
('feat',selector),
('clf',clf)])


SelectKBest will return K of the best features. I suppose then that those will be used to train the model.

Do I need to predict new tweets on the same features? Or can I just leave the pipeline as is and use CV to find the best K?