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#' @export
makeRLearner.classif.sda = function() {
makeRLearnerClassif(
cl = "classif.sda",
package = "sda",
par.set = makeParamSet(
makeNumericLearnerParam("lambda", lower = 0, upper = 1),
makeNumericLearnerParam("lambda.var", lower = 0, upper = 1),
makeNumericLearnerParam("lambda.freqs", lower = 0, upper = 1),
makeLogicalLearnerParam("diagonal", default = FALSE),
makeLogicalLearnerParam("verbose", default = TRUE, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics", "prob"),
name = "Shrinkage Discriminant Analysis",
short.name = "sda",
callees = "sda"
)
}
#' @export
trainLearner.classif.sda = function(.learner, .task, .subset, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
sda::sda(Xtrain = as.matrix(d$data), L = d$target, ...)
}
#' @export
predictLearner.classif.sda = function(.learner, .model, .newdata, ...) {
p = sda::predict.sda(.model$learner.model, as.matrix(.newdata))
if (.learner$predict.type == "response") {
return(p$class)
} else {
return(p$posterior)
}
}
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