[R] caret - prevent resampling when no parameters to find

[Max Kuhn]

It isn't building the same model since each fit is created from
different data sets.

The resampling is sort of the point of the function, but if you really
want to avoid it, supply your own index in trainControl that has every
index (eg, index = seq(along = mdrrClass)). In this case, the
performance it gives is the apparent error rate.

Max

On Sun, May 1, 2011 at 5:57 PM, pdb <philb at philbrierley.com> wrote:
> I want to use caret to build a model with an algorithm that actually has no
> parameters to find.
>
> How do I stop it from repeatedly building the same model 25 times?
>
>
> library(caret)
> data(mdrr)
> LOGISTIC_model <- train(mdrrDescr,mdrrClass
>                        ,method='glm'
>                        ,family=binomial(link="logit")
>                        )
> LOGISTIC_model
>
> 528 samples
> 342 predictors
>  2 classes: 'Active', 'Inactive'
>
> Pre-processing: None
> Resampling: Bootstrap (25 reps)
>
> Summary of sample sizes: 528, 528, 528, 528, 528, 528, ...
>
> Resampling results
>
>  Accuracy  Kappa   Accuracy SD  Kappa SD
>  0.552     0.0999  0.0388       0.0776  --
> View this message in context: http://r.789695.n4.nabble.com/caret-prevent-resampling-when-no-parameters-to-find-tp3488761p3488761.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help at r-project.org mailing list
> 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.
>



-- 

Max