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#' @export
makeRLearner.classif.qda = function() {
makeRLearnerClassif(
cl = "classif.qda",
package = "MASS",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "method", default = "moment", values = c("moment", "mle", "mve", "t")),
makeNumericLearnerParam(id = "nu", default = 5, lower = 2, requires = quote(method == "t")),
makeDiscreteLearnerParam(id = "predict.method", values = c("plug-in", "predictive", "debiased"),
default = "plug-in", when = "predict")
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
name = "Quadratic Discriminant Analysis",
short.name = "qda",
note = "Learner parameter `predict.method` maps to `method` in `predict.qda`.",
callees = c("qda", "predict.qda")
)
}
#' @export
trainLearner.classif.qda = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
MASS::qda(f, data = getTaskData(.task, .subset, recode.target = "drop.levels"), ...)
}
#' @export
predictLearner.classif.qda = function(.learner, .model, .newdata, predict.method = "plug-in", ...) {
p = predict(.model$learner.model, newdata = .newdata, method = predict.method, ...)
if (.learner$predict.type == "response") {
return(p$class)
} else {
return(p$posterior)
}
}
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