select.rules: Select Decision Rules to Achieve Higher Prediction Accuracy

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

Select Decision Rules to Achieve Higher Prediction Accuracy

Description

Select rules from a extrat.rules (rules) object

Usage

select.rules(object, data, data.pair = FALSE)

Arguments

object	An extracted rule (rules) object generated from the extract.rules function.
data	A validation dataset for selecting rules.
data.pair	Is data already converted into binary ranked pairs from the pair function?

Value

rule	Interpretable selected rules. Note that the performance score displayed is inaccurate based on few samples from the original argument object.
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)
predict(objr, tnbc[101:110,1:5])$label
objrs <- select.rules(objr,tnbc[110:130,c(1:5,337)])
predict(objrs, tnbc[101:110,1:5])$label

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