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 5555 features. What shou I do? I use .setElasticNetParam(1.0), but still all the features is in lrModel.coefficients. import org.apache.spark.ml.classification.LogisticRegression val data=spark.read.format("libsvm").option("numFeatures","5555").load("/tmp/data/training_data3") val Array(trainingData, testData) = data.randomSplit(Array(0.5, 0.5), seed = 1234L) val lr = new LogisticRegression() val lrModel = lr.fit(trainingData) println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") val predictions = lrModel.transform(testData) predictions.show() Thanks, lujinhong

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?
Thanks, lujinhong

binomial. Please use in combination with onevsrest for multiclass problems in spark 2.0.2 Dhanesh +919741125245 On Sun, Mar 19, 2017 at 4:29 PM, jinhong lu <[hidden email]> wrote:

Thanks Dhanesh, and how about the features question?

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 <[hidden email]> wrote:
 Dhanesh
+919741125245 
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 nonzero 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 <[hidden email]> wrote:

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 <[hidden email]> wrote:

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