Hi,
I?ve often come across this problem and have found genetic algorithms (GA) to be
extremely useful. I wrote my first GA code in the 80?s and have extensive
experience with the method. The package rgenoud is a very full featured GA
implementation. Just code up your parameters as arguments to the function
giving your method, random forests or whatever, then define a target variable
for performance or fitness such as AUC or R^2, whatever is appropriate, and let
the GA climb to the top of the fitness landscape. If you have a large problem
you may want to speed things up by using parallel processes across cores or
machines. Rgenoud handles that well.
Good luck!
James
> On Oct 11, 2019, at 4:21 PM, javed khan <javedbtk111 at gmail.com>
wrote:
>
> Hi
>
> I will appreciate if someone provide the link to some tutorials/videos
> where parameters running are performed in R. For instance, if we have to
> perform predictions/classification using random forest or other algorithm,
> how different optimization algorithms tune the parameters of random forest
> such as numbers of trees etc.
>
> Best regards
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>