blackboost: 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(),
           tree_controls = ctree_control(
               teststat = "max",
               testtype = "Teststatistic",
               mincriterion = 0,
               maxdepth = 2, savesplitstats = FALSE), 
           ...)

Arguments

formula

a symbolic description of the model to be fit.

data

a data frame containing the variables in the model.

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-learner

...

additional arguments passed to mboost_fit, including weights, offset, family and control. For default values see

Value

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

Details

This function implements the `classical' gradient boosting utilizing regression trees as base-learners. Essentially, the same algorithm is implemented in package gbm. 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.

References

Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477--505.

Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651--674.

Yoav Freund and Robert E. Schapire (1996), Experiments with a new boosting algorithm. In Machine Learning: Proc. Thirteenth International Conference, 148--156.

Jerome H. Friedman (2001), Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, 1189--1232.

Greg Ridgeway (1999), The state of boosting. Computing Science and Statistics, 31, 172--181.

Examples

Run this code

### 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
    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)

Run the code above in your browser using DataLab

Get 50% off unlimited learning