BlackBoostModel {MachineShop}

Gradient Boosting with Regression Trees

Description

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

Usage

BlackBoostModel(
  family = NULL,
  mstop = 100,
  nu = 0.1,
  risk = c("inbag", "oobag", "none"),
  stopintern = FALSE,
  trace = FALSE,
  teststat = c("quadratic", "maximum"),
  testtype = c("Teststatistic", "Univariate", "Bonferroni", "MonteCarlo"),
  mincriterion = 0,
  minsplit = 10,
  minbucket = 4,
  maxdepth = 2,
  saveinfo = FALSE,
  ...
)

Arguments

family

optional Family object. Set automatically according to the class type of the response variable.

mstop

number of initial boosting iterations.

nu

step size or shrinkage parameter between 0 and 1.

risk

method to use in computing the empirical risk for each boosting iteration.

stopintern

logical inidicating whether the boosting algorithm stops internally when the out-of-bag risk increases at a subsequent iteration.

trace

logical indicating whether status information is printed during the fitting process.

teststat

type of the test statistic to be applied for variable selection.

testtype

how to compute the distribution of the test statistic.

mincriterion

value of the test statistic or 1 - p-value that must be exceeded in order to implement a split.

minsplit

minimum sum of weights in a node in order to be considered for splitting.

minbucket

minimum sum of weights in a terminal node.

maxdepth

maximum depth of the tree.

saveinfo

logical indicating whether to store information about variable selection in info slot of each partynode.

...

additional arguments to ctree_control.

Details

Response types:

binary factor, BinomialVariate, NegBinomialVariate, numeric, PoissonVariate, Surv

Automatic tuning of grid parameters:

mstop, maxdepth

Default argument values and further model details can be found in the source See Also links below.

Value

MLModel class object.

See Also

blackboost, Family, ctree_control, fit, resample

Examples

## Requires prior installation of suggested packages mboost and partykit to run

data(Pima.tr, package = "MASS")

fit(type ~ ., data = Pima.tr, model = BlackBoostModel)