bst (version 0.3-2)

bst: Boosting for Classification and Regression

Description

Gradient boosting for optimizing hinge or squared error loss functions with componentwise linear, smoothing splines, tree models as base learners.

Usage

bst(x, y, cost = 0.5, family = c("hinge", "gaussian"), ctrl = bst_control(), 
control.tree = list(maxdepth = 1), learner = c("ls", "sm", "tree"))
## S3 method for class 'bst':
print(x, ...)
## S3 method for class 'bst':
predict(object, newdata=NULL, newy=NULL, mstop=NULL, type=c("response", "all.res", "class", "loss", "error"), ...)
## S3 method for class 'bst':
plot(x, type = c("step", "norm"),...)
## S3 method for class 'bst':
coef(object, ...)
## S3 method for class 'bst':
fpartial(object, mstop=NULL, newdata=NULL)

Arguments

x

a data frame containing the variables in the model.

y

vector of responses. y must be in {1, -1} for family = "hinge".

cost

price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost.

family

family = "hinge" for hinge loss and family="gaussian" for squared error loss. Implementing the negative gradient corresponding to the loss function to be minimized. By default, hinge loss for +1/-

ctrl

an object of class bst_control.

control.tree

control parameters of rpart.

learner

a character specifying the component-wise base learner to be used: ls linear models, sm smoothing splines, tree regression trees.

type

in predict a character indicating whether the response, all responses across the boosting iterations, classes, loss or classification errors should be predicted in case of hinge problems. in plot, plo

object

class of bst.

newdata

new data for prediction with the same number of columns as x.

newy

new response.

mstop

boosting iteration for prediction.

...

additional arguments.

Value

• An object of class bst with print, coef, plot and predict methods are available for linear models. For nonlinear models, methods print and predict are available.

Details

A linear or nonlinear classifier is fitted using a boosting algorithm for +1/-1 responses.

References

Zhu Wang (2011), HingeBoost: ROC-Based Boost for Classification and Variable Selection. The International Journal of Biostatistics, 7(1), Article 13.

See Also

cv.bst for cross-validated stopping iteration. Furthermore see bst_control

Examples

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)
dat.m <- bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls")
dat.m1 <- bst(x, y, ctrl = bst_control(twinboost=TRUE, 
f.init=predict(dat.m), xselect.init = dat.m$xselect, mstop=50))