This function extract the structure of a tree from a
randomForest object.
Usage
getTree(rfobj, k=1, labelVar=FALSE)
Arguments
rfobj
a randomForest object.
k
which tree to extract?
labelVar
Should better labels be used for splitting variables
and predicted class?
Details
For numerical predictors, data with values of the variable less than
or equal to the splitting point go to the left daughter node.
For categorical predictors, the splitting point is represented by an
integer, whose binary expansion gives the identities of the categories
that goes to left or right. For example, if a predictor has four
categories, and the split point is 13. The binary expansion of 13 is
(1, 0, 1, 1) (because 13 = 1*2^0 + 0*2^1 + 1*2^2 + 1*2^3), so cases with
categories 1, 3, or 4 in this predictor get sent to the left, and the rest
to the right.
Value
A matrix (or data frame, if labelVar=TRUE) with six columns and
number of rows equal to total number of nodes in the tree. The six
columns are:
left daughter
the row where the left daughter node is; 0 if the
node is terminal
right daughter
the row where the right daughter node is; 0 if
the node is terminal
split var
which variable was used to split the node; 0 if the
node is terminal
split point
where the best split is; see Details for
categorical predictor
status
is the node terminal (-1) or not (1)
prediction
the prediction for the node; 0 if the node is not
terminal