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
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 spar
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 写道：
Dhanesh
Thanks,
Thanks Dhanesh, and how about the features question?
> 在 2017年3月19日，19:08，Dhanesh Padmanabhan 写道：
>
> Dhanesh
Thanks,
lujinhong
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 I do the same thing in
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 LogisticRegressionModel
I train my LogisticRegressionModel like this, I want my model to retain only
some of the features(e.g. 500 of them), not all the features. What shou I
do?
I use .setElasticNetParam(1.0), but still all the features is in
lrModel.coefficients.
import org.apache.spark.ml.classifi
