rf.out.of.bag | R Documentation |
It runs standard random forests with out-of-bag error estimation for both classification and regression using ranger
.
This is merely a wrapper that extracts relevant info from ranger
output.
rf.out.of.bag(
x,
y,
ntree = 500,
verbose = FALSE,
sparse = FALSE,
imp_pvalues = FALSE
)
x |
Training data: data.matrix or data.frame. |
y |
A response vector. If a factor, classification is assumed, otherwise regression is assumed. |
ntree |
The number of trees. |
verbose |
A boolean value indicates if showing computation status and estimated runtime. |
sparse |
A boolean value indicates if the input matrix transformed into sparse matrix for rf modeling. |
imp_pvalues |
If compute both importance score and pvalue for each feature. |
Ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported.
Object of class rf.out.of.bag
with elements including the ranger object and critical metrics for model evaluation.
Shi Huang
Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software 77:1-17.
ranger
set.seed(123)
x <- data.frame(rbind(t(rmultinom(7, 75, c(.201,.5,.02,.18,.099))),
t(rmultinom(8, 75, c(.201,.4,.12,.18,.099))),
t(rmultinom(15, 75, c(.011,.3,.22,.18,.289))),
t(rmultinom(15, 75, c(.091,.2,.32,.18,.209))),
t(rmultinom(15, 75, c(.001,.1,.42,.18,.299)))))
y<-factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15)))
y<-factor(c(rep("A", 20), rep("B", 20), rep("C", 20)))
rf.out.of.bag(x, y, imp_pvalues=FALSE)
rf.out.of.bag(x, y, imp_pvalues=TRUE)
x_ <- data.frame(rbind(t(rmultinom(7, 7500, rep(c(.201,.5,.02,.18,.099), 1000))),
t(rmultinom(8, 750, rep(c(.201,.4,.12,.18,.099), 1000))),
t(rmultinom(15, 750, rep(c(.011,.3,.22,.18,.289), 1000))),
t(rmultinom(15, 750, rep(c(.091,.2,.32,.18,.209), 1000))),
t(rmultinom(15, 750, rep(c(.001,.1,.42,.18,.299), 1000)))))
y_<-factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15)))
rf.out.of.bag(x_, y_, imp_pvalues=FALSE)
rf.out.of.bag(x, y_, imp_pvalues=TRUE)
y0<-factor(c(rep("old", 30), rep("young", 30)))
rf.out.of.bag(x, y0, imp_pvalues=FALSE)
rf.out.of.bag(x, y0, imp_pvalues=TRUE)
y<- 1:60
rf.out.of.bag(x, y, imp_pvalues=FALSE)
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