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| author | Xiangrui Meng <meng@databricks.com> | 2014-11-13 13:54:16 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-11-13 13:54:37 -0800 |
| commit | 5de97fc4384a8671f859cf8e2808324d0337216f (patch) | |
| tree | f2b419e1a42f160ed9825708ccea7e20d964ff38 /examples/src | |
| parent | d993a44de2bf91e93c5ad3f84d35ff4e55f4b2fb (diff) | |
| download | spark-5de97fc4384a8671f859cf8e2808324d0337216f.tar.gz spark-5de97fc4384a8671f859cf8e2808324d0337216f.tar.bz2 spark-5de97fc4384a8671f859cf8e2808324d0337216f.zip | |
[SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala and Python
The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy.
In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default.
davies atalwalkar
Author: Xiangrui Meng <meng@databricks.com>
Closes #3232 from mengxr/SPARK-4372 and squashes the following commits:
9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments
d3ba096 [Xiangrui Meng] change 'none' back to None
1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
(cherry picked from commit 32218307edc6de2b08d5f7a0db6d566081d27197)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Diffstat (limited to 'examples/src')
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala | 2 | ||||
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala | 2 |
2 files changed, 2 insertions, 2 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala index 1edd2432a0..a113653810 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala @@ -55,7 +55,7 @@ object BinaryClassification { stepSize: Double = 1.0, algorithm: Algorithm = LR, regType: RegType = L2, - regParam: Double = 0.1) extends AbstractParams[Params] + regParam: Double = 0.01) extends AbstractParams[Params] def main(args: Array[String]) { val defaultParams = Params() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala index e1f9622350..6815b1c052 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala @@ -47,7 +47,7 @@ object LinearRegression extends App { numIterations: Int = 100, stepSize: Double = 1.0, regType: RegType = L2, - regParam: Double = 0.1) extends AbstractParams[Params] + regParam: Double = 0.01) extends AbstractParams[Params] val defaultParams = Params() |