Error tuning a polynomial SVM

Question

I'm trying to tune a polynomial SVM in R with the following command

svmPFitReduced <- train(
  x=dataTrain[,predModelContinuous],
  y=dataTrain[,outcome], 
  method = "svmPoly", 
  maxit = 1000, 
  metric = "ROC", 
  tuneGrid = svmPGrid, 
  trControl = trCtrl
)

but I'm finding the error

Error in train.default(x = dataTrain[, predModelContinuous], y = dataTrain[,  : 
  final tuning parameters could not be determined

The data set structure is

str(dataTrain)
'data.frame':   40001 obs. of  42 variables:
 $ PolNum     : num  2e+08 2e+08 2e+08 2e+08 2e+08 ...
 $ sex        : Factor w/ 2 levels "Male","Female": 1 1 1 2 1 2 1 1 1 2 ...
 $ type       : Factor w/ 6 levels "A","B","C","D",..: 3 1 1 2 2 4 3 3 3 2 ...
 $ catgry     : Ord.factor w/ 3 levels "Large"<"Medium"<..: 2 2 2 3 3 3 3 2 2 2 ...
 $ occup      : Factor w/ 5 levels "Employed","Housewife",..: 2 1 1 1 5 4 1 1 4 2 ...
 $ age        : num  48 23 23 39 24 39 28 43 45 38 ...
 $ group      : Factor w/ 20 levels "1","2","3","4",..: 15 16 12 16 14 8 16 9 12 8 ...
 $ bonus      : Ord.factor w/ 21 levels "-50"<"-40"<"-30"<..: 14 8 4 3 5 2 5 5 1 15 ...
 $ poldur     : num  7 1 1 14 2 4 11 2 8 5 ...
 $ value      : num  1120 21755 18430 11930 24850 ...
 $ adind      : Factor w/ 2 levels "No","Yes": 2 1 1 2 1 2 2 2 1 1 ...
 $ Pcode      : chr  "SC22" "CT109" "MA1" "SA12" ...
 $ Area       : Factor w/ 10 levels "CT","JU","MA",..: 7 1 3 6 6 6 6 4 1 2 ...
 $ Density    : num  270.5 57.3 43.2 167.9 169.8 ...
 $ Prem       : num  1159 532 527 197 908 ...
 $ Premad     : num  53.1 413.7 410.7 61.6 824.6 ...
 $ numclm     : num  0 1 0 1 0 0 0 1 0 0 ...
 $ Invite     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ Renewaltp  : num  1302 928 632 291 960 ...
 $ Renewalad  : num  58.4 599 440.4 71.3 682 ...
 $ Markettp   : num  1110 884 565 253 833 ...
 $ Marketad   : num  53.4 611.4 431.6 55.5 587 ...
 $ Premtot    : num  1212 532 527 259 908 ...
 $ Renewaltot : num  1361 928 632 362 960 ...
 $ Markettot  : num  1163 884 565 309 833 ...
 $ Renew      : Ord.factor w/ 2 levels "No"<"Yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ Premchng   : num  1.12 1.74 1.2 1.4 1.06 ...
 $ Compmeas   : num  1.17 1.05 1.12 1.17 1.15 ...
 $ numclmRec  : Ord.factor w/ 3 levels "None"<"One"<"Two or more": 1 2 1 2 1 1 1 2 1 1 ...
 $ PremChngRec: Factor w/ 20 levels "[0.546,0.758)",..: 16 20 18 19 14 3 7 19 17 11 ...
 $ ageRec     : Factor w/ 20 levels "[19,22)","[22,25)",..: 14 2 2 9 2 9 4 11 12 9 ...
 $ valueRec   : Factor w/ 20 levels "[ 1005, 3290)",..: 1 15 13 9 17 5 12 12 19 1 ...
 $ densityRec : Factor w/ 20 levels "[ 14.4, 25.0)",..: 19 6 5 15 15 13 15 1 5 11 ...
 $ CompmeasRec: Factor w/ 20 levels "[0.716,0.869)",..: 12 6 10 13 12 18 11 16 18 14 ...
 $ poldurRec  : Ord.factor w/ 16 levels "1"<"2"<"3"<"4"<..: 7 1 1 14 2 4 11 2 8 5 ...
 $ ageST      : num  0.407 -1.34 -1.34 -0.222 -1.27 ...
 $ numclmST   : num  -0.433 1.627 -0.433 1.627 -0.433 ...
 $ PremchngST : num  0.591 3.709 0.98 1.985 0.265 ...
 $ valueST    : num  -1.462 0.499 0.183 -0.434 0.793 ...
 $ DensityST  : num  1.918 -0.748 -0.924 0.636 0.659 ...
 $ CompmeasST : num  0.224 -0.539 -0.098 0.248 0.113 ...
 $ poldurST   : num  0.097 -1.2 -1.2 1.61 -0.984 ...

the trCtrl <- trainControl(method = "cv",summaryFunction = twoClassSummary,returnData =FALSE,classProbs = TRUE)

and predictors and outcome have been grouped as follows.

predCtg<-c("sex","type","catgry","occup","bonus","Area","adind","group") predCont<-c("age","numclm","Premchng","value","Density","Compmeas","poldur") predContStd<-c("ageST","numclmST","PremchngST","valueST","DensityST","CompmeasST‌​","poldurST") predFactorized<-c("ageRec","numclmRec","PremChngRec","CompmeasRec","poldurRec","‌​densityRec","valueRec"); outcome="Renew"; predModelContinuous<-c(predCtg,predContStd) 

I can post the dropbox link to data if needed

Thanks in advance for any help

Answer 1

Have you checked the svmPGrid values for the tuneGrid parameter? It is nowhere to be seen in the code you provided and I guess a potential cause for that particular error. Have a look at the answers of this question: Error in train.default(x, y, weights = w, ...) : final tuning parameters could not be determined

Hope this helps!