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getTree

Extract a single tree from a forest.


Description

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

Author(s)

See Also

Examples

data(iris)
## Look at the third trees in the forest.
getTree(randomForest(iris[,-5], iris[,5], ntree=10), 3, labelVar=TRUE)

randomForest

Breiman and Cutler's Random Forests for Classification and Regression

v4.6-14
GPL (>= 2)
Authors
Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.
Initial release
2018-03-22

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