#' @export
makeRLearner.classif.binomial = function() {
makeRLearnerClassif(
cl = "classif.binomial",
package = "stats",
par.set = makeParamSet(
makeDiscreteLearnerParam("link", values = c("logit", "probit", "cloglog", "cauchit", "log"),
default = "logit"),
makeLogicalLearnerParam("model", default = TRUE, tunable = FALSE)
),
par.vals = list(
model = FALSE
),
properties = c("twoclass", "numerics", "factors", "prob", "weights"),
name = "Binomial Regression",
short.name = "binomial",
note = "Delegates to `glm` with freely choosable binomial link function via learner parameter `link`. We set 'model' to FALSE by default to save memory.",
callees = c("glm", "binomial")
)
}
#' @export
trainLearner.classif.binomial = function(.learner, .task, .subset, .weights = NULL, link = "logit", ...) {
f = getTaskFormula(.task)
stats::glm(f, data = getTaskData(.task, .subset), family = stats::binomial(link = link), weights = .weights, ...)
}
#' @export
predictLearner.classif.binomial = function(.learner, .model, .newdata, ...) {
x = predict(.model$learner.model, newdata = .newdata, type = "response", ...)
levs = .model$task.desc$class.levels
if (.learner$predict.type == "prob") {
propVectorToMatrix(x, levs)
} else {
levs = .model$task.desc$class.levels
p = as.factor(ifelse(x > 0.5, levs[2L], levs[1L]))
unname(p)
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.