NAs error in caret function
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))
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