cv.rbst | R Documentation |
Cross-validated estimation of the empirical risk/error, can be used for tuning parameter selection.
cv.rbst(x, y, K = 10, cost = 0.5, rfamily = c("tgaussian", "thuber", "thinge", "tbinom", "binomd", "texpo", "tpoisson", "clossR", "closs", "gloss", "qloss"), learner = c("ls", "sm", "tree"), ctrl = bst_control(), type = c("loss", "error"), plot.it = TRUE, main = NULL, se = TRUE, n.cores=2,...)
x |
a data frame containing the variables in the model. |
y |
vector of responses. |
K |
K-fold cross-validation |
cost |
price to pay for false positive, 0 < |
rfamily |
nonconvex loss function types. |
learner |
a character specifying the component-wise base learner to be used:
|
ctrl |
an object of class |
type |
cross-validation criteria. For |
plot.it |
a logical value, to plot the estimated loss or error with cross validation if |
main |
title of plot |
se |
a logical value, to plot with standard errors. |
n.cores |
The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores. |
... |
additional arguments. |
object with
residmat |
empirical risks in each cross-validation at boosting iterations |
mstop |
boosting iteration steps at which CV curve should be computed. |
cv |
The CV curve at each value of mstop |
cv.error |
The standard error of the CV curve |
rfamily |
nonconvex loss function types. |
...
Zhu Wang
rbst
## Not run: x <- matrix(rnorm(100*5),ncol=5) c <- 2*x[,1] p <- exp(c)/(exp(c)+exp(-c)) y <- rbinom(100,1,p) y[y != 1] <- -1 x <- as.data.frame(x) cv.rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls", type="lose") cv.rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls", type="error") dat.m <- rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls") dat.m1 <- cv.rbst(x, y, ctrl = bst_control(twinboost=TRUE, coefir=coef(dat.m), xselect.init = dat.m$xselect, mstop=50), family = "thinge", learner="ls") ## End(Not run)
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