Brian,
This is all outlined in the package documentation. The final model is fit
automatically. For example, using 'verboseIter' provides details. From
?train
> knnFit1 <- train(TrainData, TrainClasses,
+ method = "knn",
+ preProcess = c("center", "scale"),
+ tuneLength = 10,
+ trControl = trainControl(method = "cv", verboseIter =
TRUE))
+ Fold01: k= 5
- Fold01: k= 5
+ Fold01: k= 7
- Fold01: k= 7
+ Fold01: k= 9
- Fold01: k= 9
+ Fold01: k=11
- Fold01: k=11
<snip>
+ Fold10: k=17
- Fold10: k=17
+ Fold10: k=19
- Fold10: k=19
+ Fold10: k=21
- Fold10: k=21
+ Fold10: k=23
- Fold10: k=23
Aggregating results
Selecting tuning parameters
Fitting model on full training set
Max
On Fri, Nov 23, 2012 at 5:52 PM, Brian Feeny <bfe...@mac.com> wrote:
>
> I am used to packages like e1071 where you have a tune step and then pass
> your tunings to train.
>
> It seems with caret, tuning and training are both handled by train.
>
> I am using train and trainControl to find my hyper parameters like so:
>
> MyTrainControl=trainControl(
> method = "cv",
> number=5,
> returnResamp = "all",
> classProbs = TRUE
> )
>
> rbfSVM <- train(label~., data = trainset,
> method="svmRadial",
> tuneGrid =
> expand.grid(.sigma=c(0.0118),.C=c(8,16,32,64,128)),
> trControl=MyTrainControl,
> fit = FALSE
> )
>
> Once this returns my ideal parameters, in this case Cost of 64, do I
> simply just re-run the whole process again, passing a grid only containing
> the specific parameters? like so?
>
>
> rbfSVM <- train(label~., data = trainset,
> method="svmRadial",
> tuneGrid = expand.grid(.sigma=0.0118,.C=64),
> trControl=MyTrainControl,
> fit = FALSE
> )
>
> This is what I have been doing but I am new to caret and want to make sure
> I am doing this correctly.
>
> Brian
>
> ______________________________________________
> R-help@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
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