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| author | Xin Ren <iamshrek@126.com> | 2016-08-31 21:39:31 -0700 |
|---|---|---|
| committer | Shivaram Venkataraman <shivaram@cs.berkeley.edu> | 2016-08-31 21:39:31 -0700 |
| commit | 7a5000f39ef4f195696836f8a4e8ab4ff5c14dd2 (patch) | |
| tree | 402f7fc410378418369b4abb4e1b3d92a1358a5c /mllib/src/main | |
| parent | d008638fbedc857c1adc1dff399d427b8bae848e (diff) | |
| download | spark-7a5000f39ef4f195696836f8a4e8ab4ff5c14dd2.tar.gz spark-7a5000f39ef4f195696836f8a4e8ab4ff5c14dd2.tar.bz2 spark-7a5000f39ef4f195696836f8a4e8ab4ff5c14dd2.zip | |
[SPARK-17241][SPARKR][MLLIB] SparkR spark.glm should have configurable regularization parameter
https://issues.apache.org/jira/browse/SPARK-17241
## What changes were proposed in this pull request?
Spark has configurable L2 regularization parameter for generalized linear regression. It is very important to have them in SparkR so that users can run ridge regression.
## How was this patch tested?
Test manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes #14856 from keypointt/SPARK-17241.
Diffstat (limited to 'mllib/src/main')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala index 0d3181d0ac..7a6ab618a1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala @@ -69,7 +69,8 @@ private[r] object GeneralizedLinearRegressionWrapper link: String, tol: Double, maxIter: Int, - weightCol: String): GeneralizedLinearRegressionWrapper = { + weightCol: String, + regParam: Double): GeneralizedLinearRegressionWrapper = { val rFormula = new RFormula() .setFormula(formula) val rFormulaModel = rFormula.fit(data) @@ -86,6 +87,7 @@ private[r] object GeneralizedLinearRegressionWrapper .setTol(tol) .setMaxIter(maxIter) .setWeightCol(weightCol) + .setRegParam(regParam) val pipeline = new Pipeline() .setStages(Array(rFormulaModel, glr)) .fit(data) |