rf.cross.validation | R Documentation |
It runs standard random forests with n-folds cross-validation error estimation for both classification and regression using rf.out.of.bag.
rf.cross.validation(
x,
y,
nfolds = 3,
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. |
nfolds |
The number of folds in the cross validation. If nfolds > length(y) or nfolds==-1, uses leave-one-out cross-validation. If nfolds was a factor, it means customized folds (e.g., leave-one-group-out cv) were set for CV. |
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. |
... |
Other parameters applicable to 'ranger'. |
Object of class rf.cross.validation
with elements including a ranger
object and mutiple metrics for model evaluations.
Shi Huang
ranger
x0 <- data.frame(t(rmultinom(16,160,c(.001,.5,.3,.3,.299))) + 0.65)
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)))))
s<-factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15)))
y<-factor(rep(c("Y", "N"), 30))
y0<-factor(c(rep("A", 10), rep("B", 30), rep("C", 5), rep("D", 15)))
system.time(rf.cross.validation(x, y, imp_pvalues=FALSE))
system.time(rf.cross.validation(x, y, imp_pvalues=TRUE))
rf.cross.validation(x, y0, imp_pvalues=FALSE)
y_n<- 1:60
rf.cross.validation(x, y_n, nfolds=5, imp_pvalues=FALSE)
rf.cross.validation(x, y_n, nfolds=5, imp_pvalues=TRUE)
# when nfolds is a factor, it actually run a leave-one-group-out cv
rf.cross.validation(x, y, nfolds=s, imp_pvalues=TRUE)
rf.cross.validation(x, y_n, nfolds=s, imp_pvalues=FALSE)
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