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#' @export
makeRLearner.classif.adaboostm1 = function() {
makeRLearnerClassif(
cl = "classif.adaboostm1",
package = "RWeka",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "P", default = 100L, lower = 90L),
makeLogicalLearnerParam(id = "Q", default = FALSE),
makeNumericLearnerParam(id = "S", default = 1L),
makeIntegerLearnerParam(id = "I", default = 10L, lower = 1L),
makeLogicalLearnerParam(id = "D", default = FALSE, requires = quote(!U)),
makeUntypedLearnerParam(id = "W", default = list("DecisionStump")),
makeLogicalLearnerParam(id = "output-debug-info", default = FALSE, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
name = "ada Boosting M1",
short.name = "adaboostm1",
note = "NAs are directly passed to WEKA with `na.action = na.pass`.",
callees = c("AdaBoostM1", "Weka_control")
)
}
#' @export
trainLearner.classif.adaboostm1 = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
ctrl = RWeka::Weka_control(...)
RWeka::AdaBoostM1(f, data = getTaskData(.task, .subset), control = ctrl, na.action = na.pass)
}
#' @export
predictLearner.classif.adaboostm1 = function(.learner, .model, .newdata, ...) {
type = switch(.learner$predict.type, prob = "prob", "class")
predict(.model$learner.model, newdata = .newdata, type = type, ...)
}
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