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Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as base-learners.
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, ... )
MLModel class object.
MLModel
optional Family object. Set automatically according to the class type of the response variable.
Family
number of initial boosting iterations.
step size or shrinkage parameter between 0 and 1.
method to use in computing the empirical risk for each boosting iteration.
logical inidicating whether the boosting algorithm stops internally when the out-of-bag risk increases at a subsequent iteration.
logical indicating whether status information is printed during the fitting process.
type of the test statistic to be applied for variable selection.
how to compute the distribution of the test statistic.
value of the test statistic or 1 - p-value that must be exceeded in order to implement a split.
minimum sum of weights in a node in order to be considered for splitting.
minimum sum of weights in a terminal node.
maximum depth of the tree.
logical indicating whether to store information about variable selection in info slot of each partynode.
info
partynode
additional arguments to ctree_control.
ctree_control
binary factor, BinomialVariate, NegBinomialVariate, numeric, PoissonVariate, Surv
binary factor
BinomialVariate
NegBinomialVariate
numeric
PoissonVariate
Surv
mstop, maxdepth
mstop
maxdepth
Default argument values and further model details can be found in the source See Also links below.
blackboost, Family, ctree_control, fit, resample
blackboost
fit
resample
# \donttest{ ## 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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