I am trying to replicate a example that I found on Tom Mitchell's Machine Learning book using R. It is a example from chapter 6. There are 14 training examples (shown bellow) of the target concept PlayTennis, where each day is described by the attributes Outlook, Temperature, Humidity, and Windy.

Training examples

    Outlook,Temperature,Humidity,Windy,Play
    overcast,cool,normal,true,yes
    overcast,hot,high,false,yes
    overcast,hot,normal,false,yes
    overcast,mild,high,true,yes
    rainy,cool,normal,false,yes
    rainy,mild,high,false,yes
    rainy,mild,normal,false,yes
    sunny,cool,normal,false,yes
    sunny,mild,normal,true,yes
    rainy,cool,normal,true,no
    rainy,mild,high,true,no
    sunny,hot,high,false,no
    sunny,hot,high,true,no
    sunny,mild,high,false,no

Here's my code

<!-- language: R -->
    library("klaR")
    library("caret")

    data = read.csv("example.csv")

    x = data[,-5]
    y = data$Play

    model = train(x,y,'nb',trControl=trainControl(method='cv',number=10))

    Outlook <- "sunny"
    Temperature <- "cool"
    Humidity <- "high"
    Windy <- "true"

    instance <- data.frame(Outlook,Temperature,Humidity,Windy)

    predict(model$finalModel,instance)


The example tries to predict the outcome for

    Outlook=sunny, Temperature=cool,Humidity=high and Wind=strong

The problem is that I am getting a different prediction from the one in the book.

Here's the probabilities I've got from my code

    no          yes
    0.001078835 0.9989212


Here's the book's probabilities

    no     yes
    0.0206 0.0053

My code classifies the unseen data as Yes and the book's classifier classifies it as No.

Shouldn't both give the same answer since we are using the same naive bayes classifier?