Do you want to get sparse model that most of the coefficients are zeros? If yes, using L1 regularization leads to sparsity. But the LogisticRegressionModel coefficients vector's size is still equal with the number of features, you can get the non-zero elements manually. Actually, it would be a sparse vector (or matrix for multinomial case) if it's sparse enough.
Thanks Yanbo On Sun, Mar 19, 2017 at 5:02 AM, Dhanesh Padmanabhan <dhanesh12...@gmail.com > wrote: > It shouldn't be difficult to convert the coefficients to a sparse vector. > Not sure if that is what you are looking for > > -Dhanesh > > On Sun, Mar 19, 2017 at 5:02 PM jinhong lu <lujinho...@gmail.com> wrote: > > Thanks Dhanesh, and how about the features question? > > 在 2017年3月19日，19:08，Dhanesh Padmanabhan <dhanesh12...@gmail.com> 写道： > > Dhanesh > > > Thanks, > lujinhong > > -- > Dhanesh > +91-9741125245 >
