Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

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

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

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

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

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 wrote: Thanks Dhanesh, and how about the features question? 在 2017年3月19日，19:08，Dhanesh Padmanabhan

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

Thanks Dhanesh, and how about the features question? > 在 2017年3月19日，19:08，Dhanesh Padmanabhan 写道： > > Dhanesh Thanks, lujinhong

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

binomial. Please use in combination with onevsrest for multi-class problems in spark 2.0.2 Dhanesh +91-9741125245 On Sun, Mar 19, 2017 at 4:29 PM, jinhong lu wrote: > By the way, I found in spark 2.1 I can use setFamily() to decide binomial > or multinomial, but how can

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

By the way, I found in spark 2.1 I can use setFamily() to decide binomial or multinomial, but how can I do the same thing in spark 2.0.2? If not support , which one is used in spark 2.0.2? binomial or multinomial? > 在 2017年3月19日，18:12，jinhong lu 写道： > > > I train my