[R-sig-ME] Help understanding an error Line Search Fails

[Ben Bolker]

  As far as I can tell none of the model types you're using fall under
the category of "mixed models" (linear/generalized linear models with
data  identified in known groups that are to be estimated by some form
of shrinkage estimator/"random effect").  (Please feel free to correct me!)

  By the way, I don't think it makes any sense to use "ProviderID" as a
*numeric* predictor variable ... that (and ProductID) are places where
you *might* actually want to use a mixed model.

  This looks like more of a CrossValidated question - note that you'll
have to provide a *reproducible* example in order to get help ...

  cheers
    Ben Bolker

On 2018-12-05 12:45 p.m., Bill Poling wrote:
> 
> 
> Good afternoon. I hope I have provided enough info to get my question answered.
> 
>> I am running windows 10 -- R3.5.1 -- RStudio Version 1.1.456
> 
> 
> Using caret package I have been comparing models using my data, a training subset N=17357.
> 
> I have run PLS, RDA, GLM, and Boosted Logit based on a couple of tutorials.
> 
> http://dataaspirant.com/2017/01/19/support-vector-machine-classifier-implementation-r-caret-package/
> 
> https://cran.r-project.org/web/packages/caret/vignettes/caret.html
> 
> https://topepo.github.io/caret/model-training-and-tuning.html
> 
> However, when I get to trying svmLinear or svmRadial they both produce error: line search fails -1.614732 -0.257144 0.00001920624 0.00001369617 -0.00000001857456 -0.00000001542947 -0.000000000000568072
> 
> I have done some googling research but cannot find a definitive answer as to why this model does not work with my data but the other models do?
> 
> https://stackoverflow.com/questions/43267209/line-search-fails-when-training-a-model-using-caret
> 
> https://stackoverflow.com/questions/15895897/line-search-fails-in-training-ksvm-prob-model
> 
> 
> 
> Any advice would be appreciated.
> 
> Thank you
> 
> WHP
> 
> str(training)
> 
> # 'data.frame':17357 obs. of  7 variables:
> #   $ SavingsReversed: num  0 0 0 0 0 ...
> # $ productID      : num  3 3 3 3 3 1 3 3 3 1 ...
> # $ ProviderID     : num  113676 114278 114278 114278 114278 ...
> # $ ModCnt         : num  0 1 1 1 1 1 1 0 0 1 ...
> # $ B2             : num  -1 -1 -1 -1 -1 -1 7 9 9 -1 ...
> # $ B1a            : num  1 1 1 1 1 1 26 26 26 3 ...
> # $ EditnumberI    : Factor w/ 2 levels "Bad","Good": 1 2 2 2 2 2 1 1 2 2 ...
> 
> 
> head(training, n=25)
> 
> # SavingsReversed productID ProviderID ModCnt B2 B1a EditnumberI
> # 1             0.00         3     113676      0 -1   1         Bad
> # 5             0.00         3     114278      1 -1   1        Good
> # 6             0.00         3     114278      1 -1   1        Good
> # 7             0.00         3     114278      1 -1   1        Good
> # 8             0.00         3     114278      1 -1   1        Good
> # 10            0.00         1     114278      1 -1   1        Good
> # 12          128.25         3     116641      1  7  26         Bad
> # 13          159.60         3     116641      0  9  26         Bad
> # 14            0.00         3     116641      0  9  26        Good
> # 15            0.00         1     117280      1 -1   3        Good
> # 16         1622.55         3     117439      1  9  26        Good
> # 17           60.07         3     117439      1  9  26        Good
> # 18            0.00         3     117439      0 -1   3        Good
> # 19          190.00         3     117962      0  9  26        Good
> # 20          372.66         3     119316      0  1  26         Bad
> # 22            0.00         3     120431      1 -1   1        Good
> # 25            0.00         3     121319      1  7  26         Bad
> # 26           18.79         3     121319      1  7  26         Bad
> # 27           23.00         3     121319      1  7  26         Bad
> # 28           18.79         3     121319      1  7  26         Bad
> # 29            0.00         3     121319      1  7  26         Bad
> # 30           25.86         3     121319      2  7  26         Bad
> # 31           14.00         3     121319      1  7  26         Bad
> # 36          113.00         3     121545      1  1  26         Bad
> # 37          197.20         3     121545      1  9  26         Bad
> 
> 
> anyNA(training)
> #[1] FALSE
> 
> My scripts
> 
> ctrl <- trainControl(
>   method = "repeatedcv",
>   repeats = 3,
>   classProbs = TRUE,
>   summaryFunction = twoClassSummary
> )
> 
> set.seed(123)
> svm_Linear <- train(EditnumberI ~., data = training,
>                     method = "svmLinear",
>                     trControl = ctrl,
>                     preProcess = c("center", "scale"),
>                     tuneLength = 10,
>                     metric="ROC")
> #warnings()
> svm_Linear
> 
> 
> 
> set.seed(123)
> svm_Radial <- train(EditnumberI ~., data = training,
>                     method = "svmRadial",
>                     trControl = ctrl,
>                     preProcess = c("center", "scale"),
>                     tuneLength = 10,
>                     metric="ROC")
> #warnings()
> svm_Radial
> 
> 
> 
> line search fails -1.614732 -0.257144 0.00001920624 0.00001369617 -0.00000001857456 -0.00000001542947 -0.000000000000568072
> 
> 
> 
> WHP
> 
> 
> 
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