GitHub user hhbyyh opened a pull request:
https://github.com/apache/spark/pull/17862
[SPARK-20602] [ML]Adding LBFGS as optimizer for LinearSVC
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-20602
Currently LinearSVC in Spark only supports OWLQN as the optimizer ( check
https://issues.apache.org/jira/browse/SPARK-14709). I made comparison between
LBFGS and OWLQN on several public dataset and found LBFGS converges much faster
for LinearSVC in most cases.
The following table presents the number of training iterations and f1 score
of both optimizers until convergence
data source:
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
training code: new LinearSVC().setMaxIter(10000).setTol(1e-6)
LBFGS requires far less iterations in most cases and probably is a better
default optimizer.
## How was this patch tested?
strengthen existing unit tests
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/hhbyyh/spark svclbfgs
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17862.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #17862
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commit d46e5edfdf5d052c59677f58767bdbe0803dc368
Author: Yuhao Yang <[email protected]>
Date: 2017-05-04T19:13:41Z
add lbfgs as default optimizer of LinearSVC
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