#' @export
makeRLearner.regr.earth = function() {
makeRLearnerRegr(
cl = "regr.earth",
package = "earth",
par.set = makeParamSet(
makeLogicalLearnerParam(id = "keepxy", default = FALSE, tunable = FALSE),
makeNumericLearnerParam(id = "trace", default = 0, upper = 10, tunable = FALSE),
makeIntegerLearnerParam(id = "degree", default = 1L, lower = 1L),
makeNumericLearnerParam(id = "penalty"),
makeIntegerLearnerParam(id = "nk", lower = 0L),
makeNumericLearnerParam(id = "thresh", default = 0.001),
makeIntegerLearnerParam(id = "minspan", default = 0L),
makeIntegerLearnerParam(id = "endspan", default = 0L),
makeNumericLearnerParam(id = "newvar.penalty", default = 0),
makeIntegerLearnerParam(id = "fast.k", default = 20L, lower = 0L),
makeNumericLearnerParam(id = "fast.beta", default = 1),
makeDiscreteLearnerParam(id = "pmethod", default = "backward",
values = c("backward", "none", "exhaustive", "forward", "seqrep", "cv")),
makeIntegerLearnerParam(id = "nprune"),
makeIntegerLearnerParam(id = "nfold", default = 0L, requires = quote(pmethod == "cv"))
),
properties = c("numerics", "factors"),
name = "Multivariate Adaptive Regression Splines",
short.name = "earth",
callees = "earth"
)
}
#' @export
trainLearner.regr.earth = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
earth::earth(f, data = getTaskData(.task, .subset), ...)
}
#' @export
predictLearner.regr.earth = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata)[, 1L]
}
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