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#' @export
makeRLearner.regr.cubist = function() {
makeRLearnerRegr(
cl = "regr.cubist",
package = "Cubist",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "committees", default = 1L, lower = 1L, upper = 100L),
makeLogicalLearnerParam(id = "unbiased", default = FALSE),
makeIntegerLearnerParam(id = "rules", default = 100L, lower = 1L),
makeNumericLearnerParam(id = "extrapolation", default = 100, lower = 0, upper = 100),
makeIntegerLearnerParam(id = "sample", default = 0L, lower = 0L),
makeIntegerLearnerParam(id = "seed", default = sample.int(4096, size = 1) - 1L, tunable = FALSE),
makeUntypedLearnerParam(id = "label", default = "outcome"),
makeIntegerLearnerParam(id = "neighbors", default = 0L, lower = 0L, upper = 9L, when = "predict")
),
properties = c("missings", "numerics", "factors"),
name = "Cubist",
short.name = "cubist",
callees = c("cubist", "cubistControl", "predict.cubist")
)
}
#' @export
trainLearner.regr.cubist = function(.learner, .task, .subset, .weights = NULL, unbiased, rules,
extrapolation, sample, seed, label, ...) {
ctrl = learnerArgsToControl(Cubist::cubistControl, unbiased, rules, extrapolation, sample, seed, label)
d = getTaskData(.task, .subset, target.extra = TRUE)
Cubist::cubist(x = d$data, y = d$target, control = ctrl, ...)
}
#' @export
predictLearner.regr.cubist = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, ...)
}
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