R cforest()

1 개요

R cforest()

set.seed(290875)

### honest (i.e., out-of-bag) cross-classification of
### true vs. predicted classes
data("mammoexp", package = "TH.data")
table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, 
                           control = cforest_unbiased(ntree = 50)),
                           OOB = TRUE))

### fit forest to censored response
if (require("TH.data") && require("survival")) {

    data("GBSG2", package = "TH.data")
    bst <- cforest(Surv(time, cens) ~ ., data = GBSG2, 
               control = cforest_unbiased(ntree = 50))

    ### estimate conditional Kaplan-Meier curves
    treeresponse(bst, newdata = GBSG2[1:2,], OOB = TRUE)

    ### if you can't resist to look at individual trees ...
    party:::prettytree(bst@ensemble[[1]], names(bst@data@get("input")))
}

### proximity, see ?randomForest
iris.cf <- cforest(Species ~ ., data = iris, 
                   control = cforest_unbiased(mtry = 2))
iris.mds <- cmdscale(1 - proximity(iris.cf), eig = TRUE)
op <- par(pty="s")
pairs(cbind(iris[,1:4], iris.mds$points), cex = 0.6, gap = 0, 
      col = c("red", "green", "blue")[as.numeric(iris$Species)],
      main = "Iris Data: Predictors and MDS of Proximity Based on cforest")
par(op)

2 같이 보기

• R 랜덤포레스트

3 참고

• https://www.rdocumentation.org/packages/party/versions/1.3-4/topics/cforest