R/LearnerRegr.R

In mllg/mlr3: Machine Learning in R - Next Generation

#' @title Regression Learner
#'
#' @usage NULL
#' @format [R6::R6Class] object inheriting from [Learner].
#' @include Learner.R
#'
#' @description
#' This Learner specializes [Learner] for regression problems.
#'
#' Many predefined learners can be found in the [mlr3misc::Dictionary] [mlr_learners] after loading the \CRANpkg{mlr3learners} package.
#'
#' @section Construction:
#' ```
#' l = LearnerRegr$new(id, param_set = ParamSet$new(), predict_types = character(), feature_types = character(),
#'     properties = character(), data_formats = "data.table", packages = character())
#' ```
#' For a description of the arguments, see [Learner].
#' `task_type` is set to `"regr"`.
#'
#' Possible values for `predict_types` are passed to and converted by [PredictionRegr]:
#' * `"response"`: Predicts a numeric response for each observation in the test set.
#' * `"se"`: Predicts the standard error for each value of response for each observation in the test set.
#'
#' @section Fields:
#' See [Learner].
#'
#' @section Methods:
#' See [Learner].
#'
#' @family Learner
#' @seealso
#' Example regression learners: [`regr.rpart`][mlr_learners_regr.rpart]
#' @export
#' @examples
#' # get all regression learners from mlr_learners:
#' lrns = mlr_learners$mget(mlr_learners$keys("^regr"))
#' names(lrns)
#'
#' # get a specific learner from mlr_learners:
#' mlr_learners$get("regr.rpart")
#' lrn("classif.featureless")
LearnerRegr = R6Class("LearnerRegr", inherit = Learner,
  public = list(
    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character()) {
      super$initialize(id = id, task_type = "regr", param_set = param_set, feature_types = feature_types,
        predict_types = predict_types, properties = properties, data_formats = data_formats, packages = packages)
    }
  )
)