Max,
Thanks, I do understand that the final model is fitted. I think I was not
clear in my posting. I am changing datasets between tuning and real training.
So maybe I tune on "trainset" but its only 5000 rows, doing my gridsearch and
all that, and then once I have the hyper parameters, I will use an increased
trainset size, say 25000.
So between tuning and training, I am modifying the trainset, specifically
making it bigger. So I have to re-fit the model and I guess what I am trying
to make sure of is that really the
only thing I need to "carry over" so to speak, is the hyper parameters I was
looking for. Is this correct? That is what I am doing, and simply passing a
grid with my specific 2 hyperparameters, to avoid it doing any type of search.
Brian
On Nov 23, 2012, at 6:06 PM, Max Kuhn wrote:
> 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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