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#' @export
makeRLearner.classif.JRip = function() {
makeRLearnerClassif(
cl = "classif.JRip",
package = "RWeka",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "F", default = 3L, lower = 2L),
makeNumericLearnerParam(id = "N", default = 2, lower = 0),
makeIntegerLearnerParam(id = "O", default = 2L, lower = 1L),
makeLogicalLearnerParam(id = "D", default = FALSE, tunable = FALSE),
makeIntegerLearnerParam(id = "S", tunable = FALSE),
makeLogicalLearnerParam(id = "E", default = FALSE),
makeLogicalLearnerParam(id = "P", default = FALSE),
makeLogicalLearnerParam(id = "output-debug-info", default = FALSE, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "missings", "numerics", "factors", "prob"),
name = "Propositional Rule Learner",
short.name = "jrip",
note = "NAs are directly passed to WEKA with `na.action = na.pass`.",
callees = c("JRip", "Weka_control")
)
}
#' @export
trainLearner.classif.JRip = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
ctrl = RWeka::Weka_control(..., S = as.integer(runif(1, min = -.Machine$integer.max, max = .Machine$integer.max)))
RWeka::JRip(f, data = getTaskData(.task, .subset), control = ctrl, na.action = na.pass)
}
#' @export
predictLearner.classif.JRip = function(.learner, .model, .newdata, ...) {
type = switch(.learner$predict.type, prob = "prob", "class")
predict(.model$learner.model, newdata = .newdata, type = type, ...)
}
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