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#' @export
makeRLearner.classif.linDA = function() {
makeRLearnerClassif(
cl = "classif.linDA",
package = "DiscriMiner",
par.set = makeParamSet(
# makeNumericVectorLearnerParam(id = "prior", lower = 0, upper = 1, default = NULL),
makeDiscreteLearnerParam(id = "validation", values = list(crossval = "crossval", learntest = "learntest", NULL = NULL), default = NULL, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics"),
name = "Linear Discriminant Analysis",
short.name = "linda",
note = "Set `validation = NULL` by default to disable internal test set validation.",
callees = c("linDA", "classify")
)
}
#' @export
trainLearner.classif.linDA = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "drop.levels")
DiscriMiner::linDA(variables = d$data, group = d$target, ...)
}
#' @export
predictLearner.classif.linDA = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
p = DiscriMiner::classify(m, newdata = .newdata)
# p$scores #we loose this information
p$pred_class
}
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