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#' @export
makeRLearner.classif.C50 = function() {
makeRLearnerClassif(
cl = "classif.C50",
package = "C50",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "trials", lower = 1L, default = 1L),
makeLogicalLearnerParam(id = "rules", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "subset", default = FALSE),
# FIXME: Default = 0 throws error because 'lower' = 2L is above default.
makeIntegerLearnerParam(id = "bands", lower = 2L, upper = 1000L,
tunable = FALSE, requires = quote(rules == TRUE)),
makeLogicalLearnerParam(id = "winnow", default = FALSE),
makeLogicalLearnerParam(id = "noGlobalPruning", default = FALSE),
makeNumericLearnerParam(id = "CF", lower = 0, upper = 1, default = 0.25),
# FIXME: upper limit is data dependent
makeIntegerLearnerParam(id = "minCases", lower = 0L, upper = Inf, default = 2L),
makeLogicalLearnerParam(id = "fuzzyThreshold", default = FALSE),
makeNumericLearnerParam(id = "sample", lower = 0, upper = .999, default = 0, tunable = TRUE),
makeIntegerLearnerParam(id = "seed", lower = -Inf, upper = Inf, tunable = FALSE),
makeLogicalLearnerParam(id = "earlyStopping", default = TRUE),
# label just changes the word 'outcome' to something else in the output
makeUntypedLearnerParam(id = "label", default = "outcome", tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "missings", "weights"),
name = "C50",
short.name = "C50",
callees = c("C5.0", "C5.0Control")
)
}
#' @export
trainLearner.classif.C50 = function(.learner, .task, .subset, .weights = NULL,
subset, bands, winnow, noGlobalPruning, CF, minCases, fuzzyThreshold, sample,
seed, earlyStopping, label, ...) {
ctrl = learnerArgsToControl(C50::C5.0Control, bands, winnow, subset,
noGlobalPruning, CF, minCases, fuzzyThreshold, sample, seed, earlyStopping,
label)
d = getTaskData(.task, .subset, target.extra = TRUE)
C50::C5.0(x = d$data, y = d$target, control = ctrl, weights = .weights, ...)
}
#' @export
predictLearner.classif.C50 = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
pred.type = .learner$predict.type
if (pred.type == "response") {
pred.type = "class"
}
preds = predict(m, newdata = .newdata, type = pred.type, ...)
return(preds)
}
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