Naive Bayes In R

library(caret)

set.seed(7267166)
trainIndex=createDataPartition(mydata$prog, p=0.7)$Resample1
train=mydata[trainIndex, ]
test=mydata[-trainIndex, ]

## check the balance
print(table(mydata$prog))

print(table(train$prog))

library(e1071)

NBclassfier=naiveBayes(prog~science+socst, data=train)
print(NBclassfier)

printALL=function(model){
  trainPred=predict(model, newdata = train, type = "class")
  trainTable=table(train$prog, trainPred)
  testPred=predict(NBclassfier, newdata=test, type="class")
  testTable=table(test$prog, testPred)
  trainAcc=(trainTable[1,1]+trainTable[2,2]+trainTable[3,3])/sum(trainTable)
  testAcc=(testTable[1,1]+testTable[2,2]+testTable[3,3])/sum(testTable)
  message("Contingency Table for Training Data")
  print(trainTable)
  message("Contingency Table for Test Data")
  print(testTable)
  message("Accuracy")
  print(round(cbind(trainAccuracy=trainAcc, testAccuracy=testAcc),3))
}
printALL(NBclassfier)

library(naivebayes)

newNBclassifier=naive_bayes(prog~sesf+science+socst,usekernel=T, data=train)
printALL(newNBclassifier)