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Xiangrui Meng commented on SPARK-34415: --------------------------------------- [~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] ) > -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org