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https://issues.apache.org/jira/browse/SPARK-18755?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16239754#comment-16239754
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Apache Spark commented on SPARK-18755:
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User 'tengpeng' has created a pull request for this issue:
https://github.com/apache/spark/pull/19660
> Add Randomized Grid Search to Spark ML
> --------------------------------------
>
> Key: SPARK-18755
> URL: https://issues.apache.org/jira/browse/SPARK-18755
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: yuhao yang
>
> Randomized Grid Search implements a randomized search over parameters, where
> each setting is sampled from a distribution over possible parameter values.
> This has two main benefits over an exhaustive search:
> 1. A budget can be chosen independent of the number of parameters and
> possible values.
> 2. Adding parameters that do not influence the performance does not decrease
> efficiency.
> Randomized Grid search usually gives similar result as exhaustive search,
> while the run time for randomized search is drastically lower.
> For more background, please refer to:
> sklearn: http://scikit-learn.org/stable/modules/grid_search.html
> http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/
> http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
> https://www.r-bloggers.com/hyperparameter-optimization-in-h2o-grid-search-random-search-and-the-future/.
> There're two ways to implement this in Spark as I see:
> 1. Add searchRatio to ParamGridBuilder and conduct sampling directly during
> build. Only 1 new public function is required.
> 2. Add trait RadomizedSearch and create new class RandomizedCrossValidator
> and RandomizedTrainValiationSplit, which can be complicated since we need to
> deal with the models.
> I'd prefer option 1 as it's much simpler and straightforward. We can support
> Randomized grid search via some smallest change.
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