Sorry for not being more clear - I'm interested in accessing these
indices from within the trainControl summaryFunction, not afterward
(from the train object).
As for the weights, I'm referring to the weights argument passed into
train.
On Fri, Feb 10, 2012 at 5:50 AM, Max Kuhn <mxk...@gmail.com> wrote:
> I think you need to read the man pages and the four vignettes. A lot
> of your questions have answers there.
>
> If you don't specify the resampling indices, they ones generated for
> you are saved in the train object:
>
>> data(iris)
>> TrainData <- iris[,1:4]
>> TrainClasses <- iris[,5]
>>
>> knnFit1 <- train(TrainData, TrainClasses,
> + method = "knn",
> + preProcess = c("center", "scale"),
> + tuneLength = 10,
> + trControl = trainControl(method = "cv"))
> Loading required package: class
>
> Attaching package: ‘class’
>
> The following object(s) are masked from ‘package:reshape’:
>
> condense
>
> Warning message:
> executing %dopar% sequentially: no parallel backend registered
>> str(knnFit1$control$index)
> List of 10
> $ Fold01: int [1:135] 1 2 3 4 5 6 7 9 10 11 ...
> $ Fold02: int [1:135] 1 2 3 4 5 6 8 9 10 12 ...
> $ Fold03: int [1:135] 1 3 4 5 6 7 8 9 10 11 ...
> $ Fold04: int [1:135] 1 2 3 5 6 7 8 9 10 11 ...
> $ Fold05: int [1:135] 1 2 3 4 6 7 8 9 11 12 ...
> $ Fold06: int [1:135] 1 2 3 4 5 6 7 8 9 10 ...
> $ Fold07: int [1:135] 1 2 3 4 5 7 8 9 10 11 ...
> $ Fold08: int [1:135] 2 3 4 5 6 7 8 9 10 11 ...
> $ Fold09: int [1:135] 1 2 3 4 5 6 7 8 9 10 ...
> $ Fold10: int [1:135] 1 2 4 5 6 7 8 10 11 12 ...
>
> There is also a savePredictions argument that gives you the hold-out results.
>
> I'm not sure which weights you are referring to.
>
> On Fri, Feb 10, 2012 at 4:38 AM, Yang Zhang <yanghates...@gmail.com> wrote:
>> Actually, is there any way to get at additional information beyond the
>> classProbs? In particular, is there any way to find out the
>> associated weights, or otherwise the row indices into the original
>> model matrix corresponding to the tested instances?
>>
>> On Thu, Feb 9, 2012 at 4:37 PM, Yang Zhang <yanghates...@gmail.com> wrote:
>>> Oops, found trainControl's classProbs right after I sent!
>>>
>>> On Thu, Feb 9, 2012 at 4:30 PM, Yang Zhang <yanghates...@gmail.com> wrote:
>>>> I'm dealing with classification problems, and I'm trying to specify a
>>>> custom scoring metric (recall@p, ROC, etc.) that depends on not just
>>>> the class output but the probability estimates, so that caret::train
>>>> can choose the optimal tuning parameters based on this metric.
>>>>
>>>> However, when I supply a trainControl summaryFunction, the data given
>>>> to it contains only class predictions, so the only metrics possible
>>>> are things like accuracy, kappa, etc.
>>>>
>>>> Is there any way to do this that I'm looking? If not, could I put
>>>> this in as a feature request? Thanks!
>>>>
>>>> --
>>>> Yang Zhang
>>>> http://yz.mit.edu/
>>>
>>>
>>>
>>> --
>>> Yang Zhang
>>> http://yz.mit.edu/
>>
>>
>>
>> --
>> Yang Zhang
>> http://yz.mit.edu/
>>
>> ______________________________________________
>> 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
--
Yang Zhang
http://yz.mit.edu/
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