Hi,
There are many things than could be wrong:
1. What is inside "ctrl" in the trainControl argument ?
2. Your model is a classication one, but if you do not configure correctly
"ctrl" you do not get out the metrics correctly. It depends if your
model
is binary or multi-class.
3. Another thing is that if it is a classification one, you should also
check that in the "train()" you "train_label" is a factor.
On top of that, remember that your problem is not reproducible.
If you attach a portion of your data, we could create a working
"caret"
code.
Thanks,
Carlos Ortega.
On Wed, Apr 20, 2022 at 10:26 PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:
> A quick web search on 'R caret package' found a host of useful
> results, the first of which was this:
> https://topepo.github.io/caret/
> Note that the author, Max Kuhn, explicitly says there that you can
> email him with questions. I think you should do so, as you do not seem
> to be making progress here.
>
> 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 Wed, Apr 20, 2022 at 12:51 PM javed khan <javedbtk111 at
gmail.com> wrote:
> >
> > Caret produce the error: Something is wrong; all the Accuracy metric
> values
> > are missing:
> > logLoss AUC prAUC Accuracy Kappa
> > Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA
> > 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
> > Median : NA Median : NA Median : NA Median : NA Median : NA
> >
> > We (group of three) working on an assignment and could not fix this
error
> > from a few days. The error comes with the majority of the models while
> with
> > a few model (e.g. nb), the code works. The data is text-based
> > classification.
> >
> > Some Warnings are:
> >
> > Warning messages:
> > 1: In train.default(y = train_label, x = train_x, method =
"pls", ... :
> > The metric "ROC" was not in the result set. logLoss will
be used
> instead.
> > 2: model fit failed for Fold01.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320292 rows, data has 1148
> >
> > 3: model fit failed for Fold02.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320013 rows, data has 1147
> >
> > 4: model fit failed for Fold03.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320013 rows, data has 1147
> >
> > 5: model fit failed for Fold04.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320292 rows, data has 1148
> >
> > 6: model fit failed for Fold05.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320013 rows, data has 1147
> >
> > 7: model fit failed for Fold06.Rep1: ncomp=3 Error in
> > `[[<-.data.frame`(`*tmp*`, i, value = structure(c(1L, 1L, 1L, :
> > replacement has 320013 rows, data has 1147
> >
> >
> >
> > Code is
> >
> >
> > m= train(y = train_label, x = train_x,
> > method = "pls" ,
> > metric = "Accuracy",
> > ## # preProc = c("center", "scale",
"nzv"),
> > trControl = ctrl)
> >
> > p=predict(m, test_x)
> > confusionMatrix(p, as.factor(test_label))
> >
> > [[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.
>
> ______________________________________________
> 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.
>
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