| blackboost {mboost} | R Documentation |
Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as base learners.
## S3 method for class 'formula':
blackboost(formula, data = list(), weights = NULL, ...)
## S3 method for class 'matrix':
blackboost(x, y, weights = NULL, ...)
blackboost_fit(object, tree_controls =
ctree_control(teststat = "max",
testtype = "Teststatistic",
mincriterion = 0,
maxdepth = 2),
fitmem = ctree_memory(object, TRUE), family = GaussReg(),
control = boost_control(), weights = NULL)
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. |
x |
design matrix. |
y |
vector of responses. |
object |
an object of class boost_data, see boost_dpp. |
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. |
fitmem |
an object of class TreeFitMemory. |
family |
an object of class boost_family-class,
implementing the negative gradient corresponding
to the loss function to be optimized, by default,
squared error loss
for continuous responses is used. |
control |
an object of class boost_control
which defines the hyper parameters of the
boosting algorithm. |
... |
additional arguments passed to callies. |
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.
Usually, the formula based interface blackboost should be used,
the fitting procedure without data preprocessing is assessible
via blackboost_fit, for example for cross-validation.
An object of class blackboost with print
and predict methods being available.
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.
Peter Buhlmann and Torsten Hothorn (2006), Boosting algorithms: regularization, prediction and model fitting. Submitted manuscript. ftp://ftp.stat.math.ethz.ch/Research-Reports/Other-Manuscripts/buhlmann/BuehlmannHothorn_Boosting-rev.pdf
### 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")