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Apache Spark commented on SPARK-18755: -------------------------------------- User 'gnsiva' has created a pull request for this issue: https://github.com/apache/spark/pull/19716 > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org