CODEWITHSUNDEEP
rpredictionr-caret
Allen
Allen

Reputation: 31

Error with class probabilities using R caret

I'm using R caret to do classification. And I got the following error message when training:

Error in train.default(train[, predictorsNames], train[, outcomeName], : Class probabilities are needed to score models using the area under the ROC curve. Set classProbs = TRUE in the trainControl() function.

I did some searching on this problem. The following two links are discussing on similar issues. Error when I try to predict class probabilities in R - caret and R caret train Error in evalSummaryFunction: cannnot compute class probabilities for regression According to the answers given, the problem may be caused by not defining outcomeName as a factor or invalid level names. But I've already converted outcomeName to a factor, tried different level names and set classProbs=TRUE and it still doesn't work.

library(caret)
library(gbm)

The data set I used is dat , which has 6 variables. I need to do classification on the variable "FlagD60".

> dput(droplevels(head(dat,5)))
structure(list(FICO = c(689L, 689L, 689L, 783L, 783L), Line = c(4000.001686, 
3700.002962, 3600.001866, 14500.00101, 5262.002105), Balance = c(1686L, 
2962L, 1866L, 1014L, 2105L), Payment = c(53L, 79L, 33L, 21L, 
15L), Age = c(6L, 81L, 82L, 235L, 57L), FlagD60 = c(0L, 0L, 0L, 
0L, 0L)), .Names = c("FICO", "Line", "Balance", "Payment", "Age", 
"FlagD60"), row.names = c(NA, 5L), class = "data.frame")

I generated a new factor with levels "yes" and "no" for classification and split the data. Since I don't know whether the error comes this preparation stage, I left it for your reference too.

### prepare for classification ###
outcomeName <- 'FlagD60'
predictorsNames <- names(dat)[names(dat) != outcomeName]
dat$FlagD60b=ifelse(dat$FlagD60==1,'yes','no')
dat$FlagD60b=as.factor(dat$FlagD60b)
outcomeName='FlagD60b'

trainIndex=createDataPartition(dat[,outcomeName],p=0.75,list = 
                               FALSE,times=1)
train=dat[ trainIndex,]
test =dat[-trainIndex,]

Below is the result of levels(train$FlagD60b).

[1] "no"  "yes"

Then I built the model like this.

#### repeated 10-fold CV, grid, gbm ####
ctrl=trainControl(method = "repeatedcv",number = 10,repeats = 10, 
                  summaryFunction = twoClassSummary, 
                  classProbs = TRUE)

set.seed(520)
gbmfit=train(train[,predictorsNames], train[,outcomeName],
             method="gbm",
             trcontrol=ctrl,
             verbose=FALSE, 
             metric="ROC")

And this gives the error as I said above. Any ideas from you will be really appreciated.

And the output of sessionInfo() is also included for your reference.

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

attached base packages:
[1] parallel  splines   stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] plyr_1.8.4      gbm_2.1.3       survival_2.39-4 caret_6.0-73    ggplot2_2.2.1   lattice_0.20-34

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.9        magrittr_1.5       MASS_7.3-45        munsell_0.4.3      colorspace_1.3-2  
 [6] foreach_1.4.3      minqa_1.2.4        stringr_1.2.0      car_2.1-4          tools_3.3.1       
[11] nnet_7.3-12        pbkrtest_0.4-7     grid_3.3.1         gtable_0.2.0       nlme_3.1-128      
[16] mgcv_1.8-12        quantreg_5.29      MatrixModels_0.4-1 iterators_1.0.8    lme4_1.1-12       
[21] lazyeval_0.2.0     assertthat_0.1     tibble_1.2         Matrix_1.2-6       nloptr_1.0.4      
[26] reshape2_1.4.2     ModelMetrics_1.1.0 codetools_0.2-14   stringi_1.1.2      scales_0.4.1      
[31] stats4_3.3.1       SparseM_1.76

Upvotes: 3

Views: 2776

Answers (2)

hanice
hanice

Reputation: 61

I just had the same problem. I believe the issue is the parameter name in the carret::train() function should be trControl instead of trcontrol. Upper case C!

Upvotes: 1

user27484823
user27484823

Reputation: 1

Please use caret::train instead of train and the error should be resolved.

Upvotes: -1

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