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.
mlr3::Task -> mlr3::TaskSupervised -> TaskRegr
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()
new()Creates a new instance of this R6 class.
The function as_task_regr() provides an alternative way to construct regression tasks.
TaskRegr$new(id, backend, target, label = NA_character_, extra_args = list())id(character(1))
Identifier for the new instance.
backend(DataBackend)
Either a DataBackend, or any object which is convertible to a DataBackend with as_data_backend().
E.g., a data.frame() will be converted to a DataBackendDataTable.
target(character(1))
Name of the target column.
label(character(1))
Label for the new instance.
extra_args(named list())
Named list of constructor arguments, required for converting task types
via convert_task().
truth()True response for specified row_ids. Format depends on the task type.
Defaults to all rows with role "use".
TaskRegr$truth(rows = NULL)rows(positive integer())
Vector or row indices.
clone()The objects of this class are cloneable with this method.
TaskRegr$clone(deep = FALSE)deepWhether to make a deep clone.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html
Package mlr3data for more toy tasks.
Package mlr3oml for downloading tasks from https://www.openml.org.
Package mlr3viz for some generic visualizations.
Dictionary of Tasks: mlr_tasks
as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).
mlr3fselect and mlr3filters for feature selection and feature filtering.
Extension packages for additional task types:
Unsupervised clustering: mlr3cluster
Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.
Other Task:
Task,
TaskClassif,
TaskSupervised,
TaskUnsupervised,
mlr_tasks,
mlr_tasks_boston_housing,
mlr_tasks_breast_cancer,
mlr_tasks_german_credit,
mlr_tasks_iris,
mlr_tasks_mtcars,
mlr_tasks_penguins,
mlr_tasks_pima,
mlr_tasks_sonar,
mlr_tasks_spam,
mlr_tasks_wine,
mlr_tasks_zoo
task = as_task_regr(palmerpenguins::penguins, target = "bill_length_mm")
task$task_type
task$formula()
task$truth()
task$data(rows = 1:3, cols = task$feature_names[1:2])
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