Control Parameters for Boosting

Description

Specification of the number of boosting iterations, step size and other parameters for boosting algorithms.

Usage

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bst_control(mstop = 50, nu = 0.1, twinboost = FALSE, twintype=1, threshold=c("standard", 
"adaptive"), f.init = NULL, coefir = NULL, xselect.init = NULL, center = FALSE, 
trace = FALSE, numsample = 50, df = 4, s = NULL, sh = NULL, q = NULL, qh = NULL, 
fk = NULL, iter = 10, intercept = FALSE, trun=FALSE)

Arguments

mstop

an integer giving the number of boosting iterations.

nu

a small number (between 0 and 1) defining the step size or shrinkage parameter.

twinboost

a logical value: TRUE for twin boosting.

twintype

for twinboost=TRUE only. For learner="ls", if twintype=1, twin boosting with weights from magnitude of coefficients in the first round of boosting. If twintype=2, weights are correlations between predicted values in the first round of boosting and current predicted values. For learners not componentwise least squares, twintype=2.

threshold

if threshold="adaptive", the estimated function ctrl$fk is updated in every boosting step. Otherwise, no update for ctrl$fk in boosting steps. Only used if in robust loss functions with the difference convex loss.

f.init

the estimate from the first round of twin boosting. Only useful when twinboost=TRUE and learner="sm" or "tree".

coefir

the estimated coefficients from the first round of twin boosting. Only useful when twinboost=TRUE and learner="ls".

xselect.init

the variable selected from the first round of twin boosting. Only useful when twinboost=TRUE.

center

a logical value: TRUE to center covariates with mean.

trace

a logical value for printout of more details of information during the fitting process.

numsample

number of random sample variable selected in the first round of twin boosting. This is potentially useful in the future implementation.

df

degree of freedom used in smoothing splines.

s,q

truncation parameter s or frequency q of outliers for robust regression and classification. If s is missing but q is available, s may be computed as the 1-q quantile of robust loss values using conventional software.

sh, qh

threshold value or frequency qh of outliers for Huber regression family="huber" or family="rhuberDC". For family="huber", if sh is not provided, sh is then updated adaptively with the median of y-yhat where yhat is the estimated y in the last boosting iteration. For family="rhuberDC", if sh is missing but qh is available, sh may be computed as the 1-qh quantile of robust loss values using conventional software.

fk

used for robust classification. A function estimate used in difference of convex algorithm

iter

number of iteration in difference of convex algorithm

intercept

logical value, if TRUE, estimation of intercept with linear predictor model

trun

logical value, if TRUE, predicted value in each boosting iteration is truncated at -1, 1, for family="closs" in bst and rfamily="closs" in rbst

Details

Objects to specify parameters of the boosting algorithms implemented in bst, via the ctrl argument. The default value of s is -1 if family="thinge", -log(3) if family="tbinom", and 4 if family="binomd"

Value

An object of class bst_control, a list. Note fk may be updated for robust boosting.

See Also

bst