extract.rules: Extract Interpretable Decision Rules from a Random Forest...

In ranktreeEnsemble: Ensemble Models of Rank-Based Trees with Extracted Decision Rules

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]

ranktreeEnsemble documentation built on May 29, 2024, 5:05 a.m.