boost_family objects provide a convenient way to specify loss functions
and corresponding risk functions to be optimized by one of the boosting
algorithms implemented in this package.Family(ngradient, loss = NULL, risk = NULL,
offset = function(y, w) 0,
fW = function(f) rep(1, length(f)),
check_y = function(y) TRUE,
weights = TRUE, name = "user-specified")
AdaExp()
Binomial()
GaussClass()
GaussReg()
Huber(d = NULL)
Laplace()
Poisson()
CoxPH()
QuantReg(tau = 0.5, qoffset = 0.5)y, f and w implementing the
negative gradient of the loss function (which is to be minimized).y and f to be minimized (!).y, f and w,
the weighted mean of the loss function by default.y and w (weights)
for computing a scalar offset.boost_family.glmboost, gamboost or
blackboost aim at minimizing the (weighted) empirical risk function
risk(y, f, w) with respect to f. By default, the risk function is the
weighted sum of the loss function loss(y, f) but can be chosen arbitrarily.
The ngradient(y, f) function is the negative gradient of loss(y, f) with
respect to f.
For binary classification problems we assume that the response y is coded by
$-1$ and $+1$.Pre-fabricated functions for the most commonly used loss functions are available as well.
The offset function returns the population minimizers evaluated
at the response, i.e., $1/2 \log(p / (1 - p))$ for Binomial() or
AdaExp() and $(\sum w_i)^{-1} \sum w_i y_i$ for GaussReg and the median
for Huber and Laplace.
Boosting for quantile regression with the QuantReg family is
introduced in Fenske et al. (2009).
gamboost, glmboost and
blackboost for the usage of Familys. See
boost_family-class for objects resulting from a call to Family.Laplace()
Family(ngradient = function(y, f) y - f,
loss = function(y, f) (y - f)^2,
name = "My Gauss Variant")Run the code above in your browser using DataLab