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Assume that a large number of binary features are added to a dataset with two class labels c1 and c2, such that for each added feature f, the class conditional probability P(f = 0|c1) = P(f = 0|c2). What effect will the addition of such features have on the accuracy of naive Bayes and kNN respectively?

I calculated for Naive Bayes P(c1| f1=0,..,fn=0) and P(c2| f1=0,..,fn=0) obtaining then that are the same as relative frequences of both classes, but how could this affect the accuracy?

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