bst_control function

Control Parameters for Boosting

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

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: 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 in robust nonconvex loss function.
• 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: nonconvex loss tuning 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: predicted values at an iteration in the MM algorithm
• start: a logical value, if start=TRUE and fk is a vector of values, then bst iterations begin with fk. Otherwise, bst iterations begin with the default values. This can be useful, for instance, in rbst for the MM boosting algorithm.
• 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 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 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.

Returns

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

See Also

bst