extract.rules {ranktreeEnsemble}

Extract Interpretable Decision Rules from a Random Forest Model

Description

Extract rules from a random forest (rfsrc) object

Usage

extract.rules(object, subtrees = 5,
              treedepth = 2,
              digit = 2,
              pairs = TRUE)

Arguments

object

A random forest (rfsrc) object

subtrees

Number of trees to extract rules

treedepth

Tree depth. The larger the number, the longer the extracted rules are.

digit

Digit to be displayed in the extracted rules.

pairs

Are varibles in (object) generated from the pair function? Set pairs = FALSE to extract rules from regular random forest (rfsrc) object with continuous predictors.

Value

rule

Interpretable extracted rules. Note that the performance score displayed is inaccurate based on few samples.

rule.raw

Rules directly extracted from trees for prediction purpose

data

Data used to grow trees from the argument (object).

Author(s)

Ruijie Yin (Maintainer,<[e-mail]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles. Journal of Translational Medicine. 22, 140 (2024). doi: 10.1186/s12967-024-04940-2

Examples

data(tnbc)
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
objr <- extract.rules(obj)
objr$rule[,1:3]

#### extract rules from a regular random forest
library(randomForestSRC)
obj2 <- rfsrc(subtype~., data = tnbc[1:100,c(1:5,337)])
objr2 <- extract.rules(obj2, pairs = FALSE)
objr2$rule[,1:3]