blackboost: Gradient Boosting with Regression Trees

In mboost: Model-Based Boosting

Gradient Boosting with Regression Trees

Description

Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as base-learners.

Usage

blackboost(formula, data = list(),
           weights = NULL, na.action = na.pass,
           offset = NULL, family = Gaussian(), 
           control = boost_control(),
           oobweights = NULL,
           tree_controls = partykit::ctree_control(
               teststat = "quad",
               testtype = "Teststatistic",
               mincriterion = 0,
               minsplit = 10, 
               minbucket = 4,
               maxdepth = 2, 
               saveinfo = FALSE),
           ...)

Arguments

formula	a symbolic description of the model to be fit.
data	a data frame containing the variables in the model.
weights	an optional vector of weights to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs.
offset	a numeric vector to be used as offset (optional).
family	a Family object.
control	a list of parameters controlling the algorithm. For more details see boost_control.
oobweights	an additional vector of out-of-bag weights, which is used for the out-of-bag risk (i.e., if boost_control(risk = "oobag")). This argument is also used internally by cvrisk.
tree_controls	an object of class "TreeControl", which can be obtained using ctree_control. Defines hyper-parameters for the trees which are used as base-learners. It is wise to make sure to understand the consequences of altering any of its arguments. By default, two-way interactions (but not deeper trees) are fitted.
...	additional arguments passed to mboost_fit, including weights, offset, family and control. For default values see mboost_fit.

Details

This function implements the ‘classical’ gradient boosting \bibcitepmboost::nr:freund.schapire:1996,mboost::friedman2001 as reviewed by \bibcitetmboost::Buehlmann:2008:StatSci utilizing unbiased regression trees \bibcitepmboost::Hothorn:2006:JCGS as base-learners. Essentially, the same algorithm is implemented in package gbm \bibcitetmboost::ridgew99. The main difference is that arbitrary loss functions to be optimized can be specified via the family argument to blackboost whereas gbm uses hard-coded loss functions. Moreover, the base-learners (conditional inference trees, see ctree) are a little bit more flexible.

The regression fit is a black box prediction machine and thus hardly interpretable.

Partial dependency plots are not yet available; see example section for plotting of additive tree models.

Value

An object of class mboost with print and predict methods being available.

References

\bibshow

*

See Also

See mboost_fit for the generic boosting function, glmboost for boosted linear models, and gamboost for boosted additive models.

See baselearners for possible base-learners.

See cvrisk for cross-validated stopping iteration.

Furthermore see boost_control, Family and methods.

Examples

### a simple two-dimensional example: cars data
cars.gb <- blackboost(dist ~ speed, data = cars,
                      control = boost_control(mstop = 50))
cars.gb

### plot fit
plot(dist ~ speed, data = cars)
lines(cars$speed, predict(cars.gb), col = "red")

### set up and plot additive tree model
if (require("partykit")) {
    ctrl <- ctree_control(maxdepth = 3)
    viris <- subset(iris, Species != "setosa")
    viris$Species <- viris$Species[, drop = TRUE]
    imod <- mboost(Species ~ btree(Sepal.Length, tree_controls = ctrl) +
                             btree(Sepal.Width, tree_controls = ctrl) +
                             btree(Petal.Length, tree_controls = ctrl) +
                             btree(Petal.Width, tree_controls = ctrl),
                   data = viris, family = Binomial())[500]
    layout(matrix(1:4, ncol = 2))
    plot(imod)
}

mboost documentation built on July 15, 2026, 5:06 p.m.