mlr3 (version 1.1.0)

TaskRegr: Regression Task

Description

This task specializes Task and TaskSupervised for regression problems. The target column is assumed to be numeric. The task_type is set to "regr".

It is recommended to use as_task_regr() for construction. Predefined tasks are stored in the dictionary mlr_tasks.

Super classes

mlr3::Task -> mlr3::TaskSupervised -> TaskRegr

Methods

Public methods

• TaskRegr$new()

• TaskRegr$truth()

• TaskRegr$clone()

Inherited methods

• mlr3::Task$add_strata()
• mlr3::Task$cbind()
• mlr3::Task$data()
• mlr3::Task$divide()
• 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()

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Method new()

Creates a new instance of this R6 class. The function as_task_regr() provides an alternative way to construct regression tasks.

Usage

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

Arguments

id

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

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().

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Method truth()

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

Usage

TaskRegr$truth(rows = NULL)

Arguments

rows

(positive integer())
Vector or row indices. Always refers to the complete data set, even after filtering.

Returns

numeric().

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Method clone()

The objects of this class are cloneable with this method.

Usage

TaskRegr$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, TaskSupervised, TaskUnsupervised, california_housing, mlr_tasks, 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

task = as_task_regr(palmerpenguins::penguins, target = "bill_length_mm")
task$task_type
task$formula()
task$truth()
task$data(rows = 1:3, cols = task$feature_names[1:2])