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Posted to issues@spark.apache.org by "Xiangrui Meng (Jira)" <ji...@apache.org> on 2021/08/17 22:20:00 UTC
[jira] [Commented] (SPARK-34415) Use randomization as a possibly
better technique than grid search in optimizing hyperparameters
[ https://issues.apache.org/jira/browse/SPARK-34415?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17400644#comment-17400644 ]
Xiangrui Meng commented on SPARK-34415:
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[~phenry] [~srowen] The implementation doesn't do uniform sampling of the hyper-parameter search space. Instead, it samples per params and then construct the cartesian product of all combinations. I think this would significantly reduce the effectiveness of the random search. Was it already discussed?
> Use randomization as a possibly better technique than grid search in optimizing hyperparameters
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-34415
> URL: https://issues.apache.org/jira/browse/SPARK-34415
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Affects Versions: 3.0.1
> Reporter: Phillip Henry
> Assignee: Phillip Henry
> Priority: Minor
> Labels: pull-request-available
> Fix For: 3.2.0
>
>
> Randomization can be a more effective techinique than a grid search in finding optimal hyperparameters since min/max points can fall between the grid lines and never be found. Randomisation is not so restricted although the probability of finding minima/maxima is dependent on the number of attempts.
> Alice Zheng has an accessible description on how this technique works at [https://www.oreilly.com/library/view/evaluating-machine-learning/9781492048756/ch04.html]
> (Note that I have a PR for this work outstanding at [https://github.com/apache/spark/pull/31535] )
>
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