Hi, jinhong. Do you use `setRegParam`, which is 0.0 by default ?
Both elasticNetParam and regParam are required if regularization is need. val regParamL1 = $(elasticNetParam) * $(regParam) val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam) On Mon, Mar 20, 2017 at 6:31 PM, Yanbo Liang <yblia...@gmail.com> wrote: > 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 <+91%2097411%2025245> >> > >
