Classification Task
This task specializes Task and TaskSupervised for classification problems. The target column is assumed to be a factor or ordered factor. The task_type is set to "classif".
Additional task properties include:
"twoclass": The task is a binary classification problem."multiclass": The task is a multiclass classification problem.It is recommended to use as_task_classif() for construction. Predefined tasks are stored in the dictionary mlr_tasks .
data("Sonar", package = "mlbench") task = as_task_classif(Sonar, target = "Class", positive = "M") task$task_type task$formula() task$truth() task$class_names task$positive task$data(rows = 1:3, cols = task$feature_names[1:2])
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html
Package list("mlr3data") for more toy tasks.
Package list("mlr3oml") for downloading tasks from https://www.openml.org.
Package list("mlr3viz") for some generic visualizations.
Dictionary of Tasks : mlr_tasks
as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).
list("mlr3fselect") and list("mlr3filters") for feature selection and feature filtering.
Extension packages for additional task types:
Other Task: Task, TaskRegr, TaskSupervised, TaskUnsupervised, california_housing, mlr_tasks, mlr_tasks_breast_cancer, mlr_tasks_german_credit, mlr_tasks_iris, mlr_tasks_mtcars, mlr_tasks_penguins, mlr_tasks_pima, mlr_tasks_sonar, mlr_tasks_spam, mlr_tasks_wine, mlr_tasks_zoo
mlr3::Task -> mlr3::TaskSupervised -> TaskClassif
class_names: (character())
Returns all class labels of the target column.
positive: (character(1))
Stores the positive class for binary classification tasks, and `NA` for multiclass tasks. To switch the positive class, assign a level to this field.
negative: (character(1))
Stores the negative class for binary classification tasks, and `NA` for multiclass tasks.
new()Creates a new instance of this R6 class. The function as_task_classif() provides an alternative way to construct classification tasks.
TaskClassif$new(
id,
backend,
target,
positive = NULL,
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.
positive: (character(1))
Only for binary classification: Name of the positive class. The levels of the target columns are reordered accordingly, so that the first element of `$class_names` is the positive class, and the second element is the negative class.
label: (character(1))
Label for the new instance.
extra_args: (named list())
Named list of constructor arguments, required for converting task types via `convert_task()`.
truth()True response for specified row_ids. Format depends on the task type. Defaults to all rows with role "use".
TaskClassif$truth(rows = NULL)
rows: (positive integer())
Vector or row indices. Always refers to the complete data set, even after filtering.
factor().
droplevels()Updates the cache of stored factor levels, removing all levels not present in the current set of active rows. cols defaults to all columns with storage type "factor" or "ordered". Also updates the task property "twoclass"/"multiclass".
TaskClassif$droplevels(cols = NULL)
cols: (character())
Vector of column names.
Modified self.
clone()The objects of this class are cloneable with this method.
TaskClassif$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links