boostWithArcFs(x, B, data, .procArgs = NULL, metadata = NULL, initialWeights = rep.int(1, nrow(data))/nrow(data), analyzePerformance = defaultOOBPerformanceAnalysis, .boostBackendArgs = NULL)
boostWithArcX4(x, B, data, .procArgs = NULL, metadata = NULL, initialWeights = rep.int(1, nrow(data))/nrow(data), analyzePerformance = defaultOOBPerformanceAnalysis, .boostBackendArgs = NULL)
boostWithAdaBoost(x, B, data, .procArgs = NULL, metadata = NULL, initialWeights = rep.int(1, nrow(data))/nrow(data), analyzePerformance = defaultOOBPerformanceAnalysis, .boostBackendArgs = NULL)train' and 'predict' or a
function that satisfies the definition of an estimation procedure given
below. The list input will invoke a call to
buildEstimationProcedure. Function input will invoke a call to
wrapProcedure, unless the function inherits from
'estimationProcedure'. In either event, metadata may be required to
properly wrap x. See the appropriate help documentation.boostBackend comes with a switch, .formatData
(defaulted to TRUE) which will look for an argument named
formula inside .procArgs and use the value of
formula to format data. If you don't want this to happen,
or if the data is already properly formatted, include
.formatData=FALSE in metadata.x is a list, .procArgs is a named list of lists with
entries .trainArgs and .predictArgs and each list is a
named list of arguments to pass to x$train and x$predict,
respectively. If x is a function, .procArgs is a named list
of arguments to pass to x, in addition to data and
weights. See 'Examples' below.defaultOOBPerformanceAnalysis is used.
See wrapPerformanceAnalyzer for metadata that may
need to be passed to make analyzePerformance compatible with the
boostr framework.wrapProcedure, buildEstimationProcedure,
wrapPerformanceAnalyzer and/or boostBackend.boostBackend.boostr" object that is the output of
boostBackend.
boost with the appropriate reweighters,
aggregators, and metadata.