According to https://cran.r-project.org/web/packages/randomForest/randomForest.pdf, classification trees are fully grown, meaning node size = 1. However, if trees are really grown to a maximum, then shouldn't each terminal node contain a single case (data point, species, etc)? If I run:
library(randomForest)
data(iris) #150 cases
set.seed(352)
rf <- randomForest(Species ~ ., iris)
hist(treesize(rf),main ="number of nodes")
I can see that most "fully grown" trees only have about 10 nodes, meaning node size can't be equal to 1...Right?
for example, (-1) below represents a terminal node for the 134th tree in the forest. Only 8 terminal nodes!?
> getTree(rf,134)
left daughter right daughter split var split point status prediction
1 2 3 3 2.50 1 0
2 0 0 0 0.00 -1 1
3 4 5 4 1.75 1 0
4 6 7 3 4.95 1 0
5 8 9 3 4.85 1 0
6 10 11 4 1.60 1 0
7 12 13 1 6.50 1 0
8 14 15 1 5.95 1 0
9 0 0 0 0.00 -1 3
10 0 0 0 0.00 -1 2
11 0 0 0 0.00 -1 3
12 0 0 0 0.00 -1 3
13 0 0 0 0.00 -1 2
14 0 0 0 0.00 -1 2
15 0 0 0 0.00 -1 3
I would be greatful if someone can explain
"Fully grown" -> "Nothing left to split". A (node of a-) decision tree is fully grown, if all data records assigned to it hold/make the same prediction.
In the iris dataset case, once you reach a node with 50 setosa data records in it, it doesn't make sense to split it into two child nodes with 25 and 25 setosas each.