@annem wrote:
Hi,
I have a doubt wrt the extent of application of regularization that it handles the data which has so much of junk(unnecessary features-- like a phone number feature to predict house price) or it handles features which is highly related(multi collinearitty) or high degree polynomials like x,x^2,x^3 and so on… or both??
I am a bit confused on the above situation as we have other methods to remove the un necessary features like pearson coffieceint,LDA,chi-square. can the regularization replace those methods in any scenario?
Posts: 1
Participants: 1
