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#' @export
makeRLearner.classif.rrlda = function() {
makeRLearnerClassif(
cl = "classif.rrlda",
package = "!rrlda",
par.set = makeParamSet(
makeNumericVectorLearnerParam(id = "prior", len = NA_integer_),
makeNumericLearnerParam(id = "lambda", default = 0.5, lower = 0),
makeNumericLearnerParam(id = "hp", default = 0.75, lower = 0),
makeIntegerLearnerParam(id = "nssamples", default = 30L, lower = 1L),
makeIntegerLearnerParam(id = "maxit", default = 50L, lower = 1L),
makeDiscreteLearnerParam(id = "penalty", default = "L2", values = c("L1", "L2"))
),
properties = c("twoclass", "multiclass", "numerics"),
name = "Robust Regularized Linear Discriminant Analysis",
short.name = "rrlda",
callees = "rrlda"
)
}
#' @export
trainLearner.classif.rrlda = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "drop.levels")
rrlda::rrlda(x = d$data, grouping = d$target, ...)
}
#' @export
predictLearner.classif.rrlda = function(.learner, .model, .newdata, ...) {
as.factor(predict(.model$learner.model, x = .newdata, ...)$class)
}
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