조건부추론나무 install.packages("party") library(party) #sampling str(iris) set.seed(1000) sampnum <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7,0.3)) sampnum # training & testing data 구분 trData <- iris[sampnum==1,] head(trData) teData <- iris[sampnum == 2, ] head(teData) shortvar <- Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width # 학습 citreeResult <- ctree(shortvar, data=trData) # 예측값과 실제값 비교 table(predict(citreeResult), trData$Species) citreeResult2 <- ctree(shortvar, data=teData) # 테스트...
#
ctree
#
predict
#
R
#
rpart
#
rpartplot
#
Rstudio
#
의사결정나무
#
조건부추론나무
원문 링크 : R : 조건부 추론나무
