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#' @export
makeRLearner.regr.rpart = function() {
makeRLearnerRegr(
cl = "regr.rpart",
package = "rpart",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "minsplit", default = 20L, lower = 1L),
makeIntegerLearnerParam(id = "minbucket", lower = 1L),
makeNumericLearnerParam(id = "cp", default = 0.01, lower = 0, upper = 1),
makeIntegerLearnerParam(id = "maxcompete", default = 4L, lower = 0L),
makeIntegerLearnerParam(id = "maxsurrogate", default = 5L, lower = 0L),
makeDiscreteLearnerParam(id = "usesurrogate", default = 2L, values = 0:2),
makeDiscreteLearnerParam(id = "surrogatestyle", default = 0L, values = 0:1),
# we use 30 as upper limit, see docs of rpart.control
makeIntegerLearnerParam(id = "maxdepth", default = 30L, lower = 1L, upper = 30L),
makeIntegerLearnerParam(id = "xval", default = 10L, lower = 0L, tunable = FALSE)
),
par.vals = list(xval = 0L),
properties = c("missings", "numerics", "factors", "ordered", "weights", "featimp"),
name = "Decision Tree",
short.name = "rpart",
note = "`xval` has been set to `0` by default for speed.",
callees = c("rpart", "rpart.control")
)
}
#' @export
trainLearner.regr.rpart = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset)
if (is.null(.weights)) {
f = getTaskFormula(.task)
rpart::rpart(f, data = d, ...)
} else {
f = getTaskFormula(.task)
rpart::rpart(f, data = d, weights = .weights, ...)
}
}
#' @export
predictLearner.regr.rpart = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, ...)
}
#' @export
getFeatureImportanceLearner.regr.rpart = function(.learner, .model, ...) {
getFeatureImportanceLearner.classif.rpart(.learner, .model, ...)
}
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