Last data update: 2014.03.03
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R: Cross-Validation for Truncated Loss Boosting
Cross-Validation for Truncated Loss Boosting
Description
Cross-validated estimation of the empirical risk/error
for truncated loss boosting parameter selection.
Usage
cv.rbst(x, y, K = 10, cost = 0.5, rfamily = c("tgaussian", "thuber", "thinge",
"tbinom", "binomd", "texpo", "tpoisson"), learner = c("ls", "sm", "tree"),
ctrl = bst_control(), type = c("loss", "error"), plot.it = TRUE, main = NULL,
se = TRUE, n.cores=2,...)
Arguments
x |
a data frame containing the variables in the model.
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y |
vector of responses. y must be in {1, -1} for binary classification
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K |
K-fold cross-validation
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cost |
price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost .
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rfamily |
truncated loss function types.
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learner |
a character specifying the component-wise base learner to be used:
ls linear models,
sm smoothing splines,
tree regression trees.
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ctrl |
an object of class bst_control .
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type |
cross-validation criteria. For type="loss" , loss function values and type="error" is misclassification error.
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plot.it |
a logical value, to plot the estimated loss or error with cross validation if TRUE .
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main |
title of plot
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se |
a logical value, to plot with standard errors.
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n.cores |
The number of CPU cores to use. The cross-validation loop
will attempt to send different CV folds off to different cores.
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... |
additional arguments.
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Value
object with
residmat |
empirical risks in each cross-validation at boosting iterations
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mstop |
boosting iteration steps at which CV curve should be computed.
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cv |
The CV curve at each value of mstop
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cv.error |
The standard error of the CV curve
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rfamily |
truncated loss function types.
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...
See Also
bst
Examples
## 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)
Results
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