Great question Rohit. I am in my early days of ML as well and it would be great if we get some idea on this from other experts on this group.
I know we can reduce dimensions by using PCA, but i think that does not allow us to understand which factors from the original are we using in the end. - Tony L. On Mon, Aug 8, 2016 at 5:12 PM, Rohit Chaddha <rohitchaddha1...@gmail.com> wrote: > > I have a data-set where each data-point has 112 factors. > > I want to remove the factors which are not relevant, and say reduce to 20 > factors out of these 112 and then do clustering of data-points using these > 20 factors. > > How do I do these and how do I figure out which of the 20 factors are > useful for analysis. > > I see SVD and PCA implementations, but I am not sure if these give which > elements are removed and which are remaining. > > Can someone please help me understand what to do here > > thanks, > -Rohit > >
