# The Strength of the Decision in Decision Tree

I learn how to use decision tree in R

library(rpart)
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
asRules(fit)


return,

Rule number: 3 [Kyphosis=present cover=19 (23%) prob=0.58]
Start< 8.5

Rule number: 23 [Kyphosis=present cover=7 (9%) prob=0.57]
Start>=8.5
Start< 14.5
Age>=55
Age< 111

Rule number: 22 [Kyphosis=absent cover=14 (17%) prob=0.14]
Start>=8.5
Start< 14.5
Age>=55
Age>=111

Rule number: 10 [Kyphosis=absent cover=12 (15%) prob=0.00]
Start>=8.5
Start< 14.5
Age< 55

Rule number: 4 [Kyphosis=absent cover=29 (36%) prob=0.00]
Start>=8.5
Start>=14.5


If I want to make a plot of decision tree, I use the following:

library(rattle)
fancyRpartPlot(fit)


return me the following figure which is associated with the rules above.

My question is that

what does prob in each rule mean ? I am confused because when the decision of rule number 22 is absent the prob = 0.14.
I think it should be prob = 0.86 since the number indicates how strong of this decision will be absent.

Could anyone clarify on this ?

Thank you

The prob always means how likely the patient is to be present, even if the node itself is going to predict absent. A probability of 0.14 that kyphosis is present corresponds to a probability of 1 - 0.14 = 0.86 that kyphosis is absent.