[R] multilabel classification XGBoost and hyperparameter tuning
bgunter@4567 @end|ng |rom gm@||@com
Thu May 27 18:06:28 CEST 2021
One other suggestion. Per the posting guide linked below, statistical
issues such as your query on "hyperparameter tuning" are off topic on this
list, as are questions about specific nonstandard packages. You might try
posting on stats.stackexchange.com instead for help on such matters.
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, May 27, 2021 at 7:51 AM Agnes g2g <agnesg2g using hotmail.com> wrote:
> Thank you for your reply.
> As far as I can see xgboost package does not provide multilabel
> The mlr package uses a wrapper for xgboost, so I have used the package
> xgboost. But I still have the problem with the hyperparameter tuning.
> Did I understand you correctly?
> Do you have any other suggestion?
> *Van:* Bert Gunter <bgunter.4567 using gmail.com>
> *Verzonden:* donderdag 27 mei 2021 16:44
> *Aan:* Agnes g2g <agnesg2g using hotmail.com>
> *CC:* r-help using r-project.org <r-help using r-project.org>
> *Onderwerp:* Re: [R] multilabel classification XGBoost and hyperparameter
> 1. A web search on "xgboost R" brought up R package "xgboost" which you
> did not mention. Did you not first try a web search or did you find that it
> did not meet your needs?
> 2. Have you looked here:
> or here: https://cran.r-project.org/web/views/MachineLearning.html
> Cran's "task views" are a useful resource for such "does R have...?"
> Bert Gunter
> "The trouble with having an open mind is that people keep coming along and
> sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
> On Thu, May 27, 2021 at 7:29 AM Agnes g2g <agnesg2g using hotmail.com> wrote:
> Hi all,
> I want to do multilabel classification with XGBoost and tune
> With the mlr package this does not seem possible, see
> Any ideas how to solve this?
> What other packages support multilabel classification for XGBoost and has
> the possibility to tune hyperparameters?
> Thanks in advance!
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> and provide commented, minimal, self-contained, reproducible code.
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