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I have to choose proper parameters in gbm() function. Until now, I have used grid search as using train(), trainControl() functions. Recently, I found the h2o package. As using this, I can do choose proper parameters. But I can't understand what is the difference between grid search that I have used and h2o method.

What is the difference???

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gbm and h2o are two separate packages, both of which can used to fit a gradient boosted tree model to data. You cannot use h2o to tune parameters of a gbm (from gbm package) model.

For more information about the h2o package, please refer: http://h2o-release.s3.amazonaws.com/h2o/rel-lambert/5/docs-website/Ruser/rtutorial.html

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  • $\begingroup$ We can make machine learning algorithms as using each package's function. h2o package include many machine learning algorithm functions. Then what is the advantage of using h2o package compare with using each machine learning algorithm function like gbm package?? $\endgroup$
    – 서영재
    Commented Apr 11, 2017 at 5:28
  • $\begingroup$ Well, for one h2o is written in java and is useful for very large datasets since you can run algorithms in a distributed fashion in various cluster environments. The R package is an easy way to access your h2o instance. $\endgroup$
    – ahly
    Commented Apr 11, 2017 at 15:51

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