#' @export
makeRLearner.classif.earth = function() {
makeRLearnerClassif(
cl = "classif.earth",
package = c("!earth", "stats"),
par.set = makeParamSet(
makeNumericLearnerParam(id = "trace", default = 0, upper = 5, tunable = FALSE),
makeLogicalLearnerParam(id = "keepxy", default = FALSE, tunable = FALSE),
makeIntegerLearnerParam(id = "degree", default = 1L, lower = 1L),
makeNumericLearnerParam(id = "penalty"),
makeIntegerLearnerParam(id = "nk", lower = 1L),
makeNumericLearnerParam(id = "thresh", default = 0.001),
makeIntegerLearnerParam(id = "minspan", default = 0L),
makeIntegerLearnerParam(id = "endspan", default = 0L),
makeNumericLearnerParam(id = "newvar.penalty", default = 0),
makeIntegerLearnerParam(id = "fast.k", default = 20L, lower = 0L),
makeNumericLearnerParam(id = "fast.beta", default = 1),
makeDiscreteLearnerParam(id = "pmethod", default = "backward",
values = c("backward", "none", "exhaustive", "forward", "seqrep", "cv")),
makeIntegerLearnerParam(id = "nprune"),
makeIntegerLearnerParam(id = "ncross", default = 1L),
makeIntegerLearnerParam(id = "nfold", default = 0L, requires = quote(pmethod == "cv")),
makeLogicalLearnerParam(id = "stratify", default = TRUE, requires = quote(nfold > 1L)),
makeUntypedLearnerParam(id = "linpreds", default = FALSE),
makeDiscreteLearnerParam("link", values = c("logit", "probit"),
default = "logit"),
makeNumericLearnerParam(id = "maxit", default = 25L, tunable = TRUE),
makeFunctionLearnerParam(id = "allowed"),
makeNumericLearnerParam(id = "Adjust.endspan", default = 2, tunable = TRUE),
makeLogicalLearnerParam(id = "Force.weights", default = FALSE, tunable = TRUE),
makeLogicalLearnerParam(id = "Use.beta.cache", default = TRUE, tunable = FALSE),
makeLogicalLearnerParam(id = "Force.xtx.prune", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "Get.leverages", default = TRUE, tunable = FALSE),
makeNumericLearnerParam(id = "Exhaustive.tol", default = 1e-10, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"),
name = "Flexible Discriminant Analysis",
short.name = "fda",
note = "This learner performs flexible discriminant analysis using the earth algorithm. na.action is set to na.fail and only this is supported.",
callees = c("earth", "glm", "glm.control", "binomial")
)
}
#' @export
trainLearner.classif.earth = function(.learner, .task, .subset, .weights = NULL, link = "logit", maxit = 25L, ...) {
f = getTaskFormula(.task)
earth::earth(f, data = getTaskData(.task, .subset), weights = .weights, glm = list(family = binomial(link = link), maxit = maxit), ...)
}
#' @export
predictLearner.classif.earth = function(.learner, .model, .newdata, ...) {
p = predict(.model$learner.model, newdata = .newdata, type = "response", ...)
levs = .model$task.desc$class.levels
if (.learner$predict.type == "prob") {
if (length(levs) == 2) p = propVectorToMatrix(p, levs)
} else {
if (length(levs) == 2) {
p = as.factor(ifelse(p > 0.5, levs[2L], levs[1L]))
} else {
p = as.factor(predict(.model$learner.model, newdata = .newdata, type = "class", ...))
}
p = unname(p)
}
return(p)
}
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