caret train method not working (something is wrong for all accuracy results) for outcomes with >2 categories

Question

Hi I know someone asked similar issues before but no clear answer yet (or I tried their solution without success: Caret error using GBM, but not without caret Caret train method

Answer 1

Instead of passing the formula in the train function, pass values for parameters x, y, method etc

the old way:

modFit = train(data.df$Label ~ ., 
                 data = data.df, 
                method = "rpart", 
                trControl= cntr, 
                tuneLength = 7)

new way:

modFit = train(x = data.df.cols, 
                 y = data.df$Label,
                 method = "rpart",
                   trControl = cntrl, 
                   tuneLength = 7)

Note: x = data.df.cols has all columns except the label, data.df.cols = data.df[,2:ncol(data.df)]

Answer 2

You need to convert the newly created Class_new to a factor, as follows:

Sonar$Class_new<-ifelse(Sonar$Class=="R","R",ifelse(Sonar$rand>0,"M","H"))
Sonar$Class_new <- factor(Sonar$Class_new)

Also, you may want to remove the variables Class and rand from your training and testing data sets. You can do somthing like:

training <- Sonar[ inTraining, !(names(Sonar) %in% c("Class", "rand"))]
testing <- Sonar[-inTraining, !(names(Sonar) %in% c("Class", "rand"))]

Answer 3

I had allowParallel = TRUE in the train function and the machine I was working on did not have multiple cores. After I commented that statement, I did not get the error.

Answer 4

Thank howaj for your post. That did work for the data I posted but somehow did not work for another dataset, where everything seems to be the same. But I figured out finally:

Could be a syntax issue here. Instead of using train(y~., data=training, ...), I changed to the train(train$y,train$x, ...) without specifying data=.. explicitly:

train(training[,!names(training)%in%response], training$response ...)

This worked.