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

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?