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#' @export
makeRLearner.regr.extraTrees = function() {
makeRLearnerRegr(
cl = "regr.extraTrees",
package = "extraTrees",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", default = 500L, lower = 1L),
makeIntegerLearnerParam(id = "mtry", lower = 1L),
makeIntegerLearnerParam(id = "nodesize", default = 5L),
makeIntegerLearnerParam(id = "numRandomCuts", default = 1L),
makeLogicalLearnerParam(id = "evenCuts", default = FALSE),
makeIntegerLearnerParam(id = "numThreads", default = 1L, lower = 1L),
makeIntegerVectorLearnerParam(id = "subsetSizes"),
makeUntypedLearnerParam(id = "subsetGroups"),
makeIntegerVectorLearnerParam(id = "tasks"),
makeNumericLearnerParam(id = "probOfTaskCuts", lower = 0, upper = 1),
makeIntegerLearnerParam(id = "numRandomTaskCuts", default = 1L, lower = 1L),
makeDiscreteLearnerParam(id = "na.action", default = "stop",
values = c("stop", "zero", "fuse"))
),
properties = c("numerics", "weights"),
name = "Extremely Randomized Trees",
short.name = "extraTrees",
callees = "extraTrees"
)
}
#' @export
trainLearner.regr.extraTrees = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
args = c(list(x = as.matrix(d$data), y = d$target), list(...))
if (!is.null(.weights)) {
args$weights = .weights
}
do.call(extraTrees::extraTrees, args)
}
#' @export
predictLearner.regr.extraTrees = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, as.matrix(.newdata), ...)
}
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