bst_control {bst} | R Documentation |
Control Parameters for Boosting
Description
Specification of the number of boosting iterations, step size and other parameters for boosting algorithms.
Usage
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, start=FALSE, 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: |
twintype |
for |
threshold |
if |
f.init |
the estimate from the first round of twin boosting. Only useful when |
coefir |
the estimated coefficients from the first round of twin boosting. Only useful when |
xselect.init |
the variable selected from the first round of twin boosting. Only useful when |
center |
a logical value: |
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 |
nonconvex loss tuning parameter |
sh , qh |
threshold value or frequency |
fk |
predicted values at an iteration in the MM algorithm |
start |
a logical value, if |
iter |
number of iteration in the MM 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 |
Details
Objects to specify parameters of the boosting algorithms implemented in bst
, via the ctrl
argument.
The s
value is for robust nonconvex loss where smaller s
value is more robust to outliers with family="closs", "tbinom", "thinge", "tbinomd"
, and larger s
value more robust with family="clossR", "gloss", "qloss"
.
For family="closs"
, if s=2
, the loss is similar to the square loss; if s=1
, the loss function is an approximation of the hinge loss; for smaller values, the loss function approaches the 0-1 loss function if s<1
, the loss function is a nonconvex function of the margin.
The default value of s
is -1 if family="thinge"
, -log(3) if family="tbinom"
, and 4 if family="binomd"
. If trun=TRUE
, boosting classifiers can produce real values in [-1, 1] indicating their confidence in [-1, 1]-valued classification. cf. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 80-91, 1998.
Value
An object of class bst_control
, a list. Note fk
may be updated for robust boosting.