mlr3 (version 0.18.0)

TaskSupervised: Supervised Task

Description

This is the abstract base class for task objects like TaskClassif and TaskRegr. It extends Task with methods to handle a target columns. Supervised tasks for probabilistic regression (including survival analysis) can be found in mlr3proba.

Super class

mlr3::Task -> TaskSupervised

Methods

Public methods

• TaskSupervised$new()

• TaskSupervised$truth()

• TaskSupervised$clone()

Inherited methods

• mlr3::Task$add_strata()
• mlr3::Task$cbind()
• mlr3::Task$data()
• mlr3::Task$droplevels()
• mlr3::Task$filter()
• mlr3::Task$format()
• mlr3::Task$formula()
• mlr3::Task$head()
• mlr3::Task$help()
• mlr3::Task$levels()
• mlr3::Task$missings()
• mlr3::Task$print()
• mlr3::Task$rbind()
• mlr3::Task$rename()
• mlr3::Task$select()
• mlr3::Task$set_col_roles()
• mlr3::Task$set_levels()
• mlr3::Task$set_row_roles()

---

Method new()

Creates a new instance of this R6 class.

Usage

TaskSupervised$new(
  id,
  task_type,
  backend,
  target,
  label = NA_character_,
  extra_args = list()
)

Arguments

id

(character(1))
Identifier for the new instance.

task_type

(character(1))
Type of task, e.g. "regr" or "classif". Must be an element of mlr_reflections$task_types$type.

backend

(DataBackend)
Either a DataBackend, or any object which is convertible to a DataBackend with as_data_backend(). E.g., a data.frame() will be converted to a DataBackendDataTable.

target

(character(1))
Name of the target column.

label

(character(1))
Label for the new instance.

extra_args

(named list())
Named list of constructor arguments, required for converting task types via convert_task().

---

Method truth()

True response for specified row_ids. Format depends on the task type. Defaults to all rows with role "use".

Usage

TaskSupervised$truth(rows = NULL)

Arguments

rows

(positive integer())
Vector or row indices.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TaskSupervised$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html

• Package mlr3data for more toy tasks.

• Package mlr3oml for downloading tasks from https://www.openml.org.

• Package mlr3viz for some generic visualizations.

• Dictionary of Tasks: mlr_tasks

• as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).

• mlr3fselect and mlr3filters for feature selection and feature filtering.

• Extension packages for additional task types:

  • Unsupervised clustering: mlr3cluster

  • Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.

Other Task: Task, TaskClassif, TaskRegr, TaskUnsupervised, mlr_tasks, mlr_tasks_boston_housing, mlr_tasks_breast_cancer, mlr_tasks_german_credit, mlr_tasks_iris, mlr_tasks_mtcars, mlr_tasks_penguins, mlr_tasks_pima, mlr_tasks_sonar, mlr_tasks_spam, mlr_tasks_wine, mlr_tasks_zoo

Examples

TaskSupervised$new("penguins", task_type = "classif", backend = palmerpenguins::penguins,
  target = "species")

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