View source: R/ML_BlackBoostModel.R
BlackBoostModel | R Documentation |
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 and further model details can be found in the source links below.
MLModel
class object.
blackboost
, Family
,
ctree_control
, fit
,
resample
## 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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