#makeRLearner.regr.logicreg = function() {
# makeRLearnerRegr(
# cl = "regr.logicreg",
# package = "LogicReg",
# par.set = makeParamSet(
# makeIntegerLearnerParam(id="ntrees", lower=1L, upper=5L),
# makeIntegerLearnerParam(id="nleaves", lower=1L),
# makeNumericLearnerParam(id="penalty", lower=0),
# makeIntegerLearnerParam(id="seed"),
# makeDiscreteLearnerParam(id="select", default=1L, values=c(1L,6L), pass.default=TRUE),
# makeIntegerLearnerParam(id="treesize", default=8L, lower=1),
# makeDiscreteLearnerParam(id="opers", default=1L, values=1:3),
# makeIntegerLearnerParam(id="minmass", default=0L, lower=0L)
# ),
# missings = FALSE,
# numerics = TRUE,
# factors = FALSE,
# se = FALSE,
# weights = TRUE
# )
#}
#
#trainLearner.regr.logicreg = function(.learner, .task, .subset, ...) {
# xs = learnerArgsToControl(logreg.tree.control, c("treesize", "opers", "minmass"), list(...))
# d = getTaskData(.task, .subset, target.extra=TRUE)
# logreg(bin=d$data, resp=d$target, type=2, tree.control=xs$control,
# select=select, ntrees=ntrees, nleaves=nleaves, penalty=penalty, seed=seed)
#}
#
#predictLearner.regr.logicreg = function(.learner, .model, .newdata, ...) {
# predict(.model$learner.model, newbin=.newdata, ...)
#}
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