03-Generate training and test sets.Rmd

```{r}
#split the dataset into training and test sets
set.seed(1)
index = round(nrow(banking)*0.2,digits=0)
test.indices = sample(1:nrow(banking), index)
banking.train=banking[-test.indices,] #80% training set
banking.test=banking[test.indices,]   #20% test set
YTrain = banking.train$y
XTrain = banking.train %>% select(-y)
YTest = banking.test$y
XTest = banking.test %>% select(-y)
records = matrix(NA, nrow=5, ncol=2) #creating a tracking record for KNN, Logistic Regression, Decision Tree, KNN, SVM.
colnames(records) <- c("train.error","test.error") 
rownames(records) <- c("Logistic","Tree","KNN","Random Forests","SVM")
#######define the error rate function
calc_error_rate <- function(predicted.value, true.value){
  return(mean(true.value!=predicted.value)) 
}
```