I am trying to replicate a example that I found in Tom Mitchell's book Machine Learning (1997), using R. It is a example from chapter 6.
There are 14 training examples (shown below) 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 are the probabilities I've got from my code:
no yes
0.001078835 0.9989212
Here are 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?
EDIT:
I replicated the example using scikit-learn MultinomialNB classifier and I have got the following probabilities
no yes
0.769 0.231
which are similar to the normalized probabilities of the book.
Normalized probabilities of the book
no yes
0.795 0.205
