Displaying 3 results from an estimated 3 matches for "lgocv".
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lgcov
2012 Jul 12
1
Caret: Use timingSamps leads to error
...want to use the caret package and found out about the timingSamps
obtion to obtain the time which is needed to predict results. But, as
soon as I set a value for this option, the whole model generation fails.
Check this example:
-------------------------
library(caret)
tc=trainControl(method='LGOCV', timingSamps=10)
tcWithout=trainControl(method='LGOCV')
x=train(Volume~Girth+Height, method="lm", data=trees, trControl=tcWithout)
x=train(Volume~Girth+Height, method="lm", data=trees, trControl=tc)
Error in eval(expr, envir, enclos) : object 'Girth' not f...
2013 Feb 19
0
CARET. Relationship between data splitting trainControl
...eResample, createFolds and
createMultiFolds)
To better frame my questions, let me use the following example from the
documentation:
*************************************
data(BloodBrain)
set.seed(1)
tmp <- createDataPartition(logBBB,p = .8, times = 100)
trControl = trainControl(method = "LGOCV", index = tmp)
ctreeFit <- train(bbbDescr, logBBB, "ctree",trControl=trControl)
*************************************
My questions are:
1) If I use createDataPartition (which I assume that does stratified
bootstrapping), as in the above example, and I pass the result as index to...
2011 May 05
1
[caret package] [trainControl] supplying predefined partitions to train with cross validation
Hi all,
I run R 2.11.1 under ubuntu 10.10 and caret version 2.88.
I use the caret package to compare different models on a dataset. In
order to compare their different performances I would like to use the
same data partitions for every models. I understand that using a LGOCV
or a boot type re-sampling method along with the "index" argument of
the trainControl function, one is able to supply a training partition
to the train function.
However, I would like to apply a 10-fold cross validation to validate
the models and I did not find any way to supply some pre...