cross_Validation in oneR usuing R language-CodePudding

I'm trying to cross-value with the OneR algorithm and I don't quite know how to do it.With the example code I get the error "Error in x[0, , drop = FALSE] : incorrect number of dimensions"

glass <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data",
                  col.names=c("","RI","Na","Mg","Al","Si","K","Ca","Ba","Fe","Type")

str(glass)

head(glass)

standard.features <- scale(glass[,2:10])

data <- cbind(standard.features,glass[11])

data$Type<-factor(data$Type)

anyNA(data)

inTraining <- createDataPartition(data$Type, p = .7, list = FALSE, times =1 )

training <- data[ inTraining,]

testing  <- data[-inTraining,]

set.seed(12345)

fitControl <- trainControl(## 5-fold CV
  method = "cv",
  number = 5
  
)

model <- OneR(Type~.,data= training)


oneRFit1 <- train(model, 
                 trControl = fitControl)

CodePudding user response：

It is fairly easy to write your own loop to carry out cross-validation. However, it looks like you want to use the caret package to manage it. If so, just use the method argument inside caret's train function to specify that you want to use OneR:

oneRFit1 <- train(Type~., 
                  data=training,
                  method="OneR" ,
                  trControl = fitControl)

CodePudding user response：

Applying your suggestion I get the following error: predictions failed for fold1: parameter = none Error in predict.OneR(modelFit, newdata): cannot find feature column in newdata.