Gradient Boosting with Regression Trees
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,
...
)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 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 |
... |
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.
## 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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