Description Usage Arguments Details Value See Also Examples
View source: R/ML_BlackBoostModel.R
Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as baselearners.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  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,
...
)

family 
optional 
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 outofbag 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  pvalue 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 
... 
additional arguments to 
binary factor
, BinomialVariate
,
NegBinomialVariate
, numeric
, PoissonVariate
,
Surv
mstop
, maxdepth
Default values for the NULL
arguments and further model details can be
found in the source links below.
MLModel
class object.
blackboost
, Family
,
ctree_control
, fit
,
resample
1 2 3 4 5  ## Requires prior installation of suggested packages mboost and partykit to run
data(Pima.tr, package = "MASS")
fit(type ~ ., data = Pima.tr, model = BlackBoostModel)

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