I have a binary feature that i want to use it with textual features i.e. unigrams. I use logistic regression and TF/IDF for representing text. So i simply add a unique feature, say ss or oo, to text of each instances. But in practice, i see adding more number of these features to instances, say two oo or ss or more get me a better results. What is the reason? How these weights improve the classification results? Should not logistic regression can get more weights to this features instead of weighting them by hand?