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#' @title Set the type of predictions the learner should return.
#'
#' @description
#' Possible prediction types are:
#' Classification: Labels or class probabilities (including labels).
#' Regression: Numeric or response or standard errors (including numeric response).
#' Survival: Linear predictor or survival probability.
#'
#' For complex wrappers the predict type is usually also passed down the
#' encapsulated learner in a recursive fashion.
#'
#' @template arg_learner
#' @param predict.type (`character(1)`)\cr
#' Classification: \dQuote{response} or \dQuote{prob}.
#' Regression: \dQuote{response} or \dQuote{se}.
#' Survival: \dQuote{response} (linear predictor) or \dQuote{prob}.
#' Clustering: \dQuote{response} or \dQuote{prob}.
#' Default is \dQuote{response}.
#' @template ret_learner
#' @family predict
#' @family learner
#' @export
setPredictType = function(learner, predict.type) {
assertClass(learner, classes = "Learner")
UseMethod("setPredictType")
}
#' @export
setPredictType.Learner = function(learner, predict.type) {
# checks should be done down here i guess, because of recursive calls in wrappers
assertChoice(predict.type, choices = switch(learner$type,
classif = c("response", "prob"),
multilabel = c("response", "prob"),
regr = c("response", "se"),
surv = c("response", "prob"),
costsens = "response",
cluster = c("response", "prob")
))
if (predict.type == "prob" && !hasLearnerProperties(learner, "prob")) {
stopf("Trying to predict probs, but %s does not support that!", learner$id)
}
if (predict.type == "se" && !hasLearnerProperties(learner, "se")) {
stopf("Trying to predict standard errors, but %s does not support that!", learner$id)
}
learner$predict.type = predict.type
return(learner)
}
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