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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2021/02/10 08:54:00 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=17282313#comment-17282313 ]
Apache Spark commented on SPARK-34415:
--------------------------------------
User 'PhillHenry' has created a pull request for this issue:
https://github.com/apache/spark/pull/31535
> 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
> Priority: Minor
> Labels: pull-request-available
>
> 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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