Circle Classification Task Generator

A TaskGenerator for the circle binary classification task in mlbench::mlbench.circle(). Creates a large circle containing a smaller circle.

Dictionary

This TaskGenerator can be instantiated via the dictionary mlr_task_generators or with the associated sugar function tgen():

mlr_task_generators$get("circle")
tgen("circle")

Parameters

Id	Type	Default	Range	Levels
d	integer	2	\([2, \infty)\)	-

See also

• Dictionary of TaskGenerators: mlr_task_generators

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

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other TaskGenerator: TaskGenerator, mlr_task_generators_2dnormals, mlr_task_generators_cassini, mlr_task_generators_friedman1, mlr_task_generators_moons, mlr_task_generators_simplex, mlr_task_generators_smiley, mlr_task_generators_spirals, mlr_task_generators_xor, mlr_task_generators

Super class

mlr3::TaskGenerator -> TaskGeneratorCircle

Methods

Public methods

• TaskGeneratorCircle$new()

• TaskGeneratorCircle$plot()

• TaskGeneratorCircle$clone()

Inherited methods

• mlr3::TaskGenerator$format()

• mlr3::TaskGenerator$generate()

• mlr3::TaskGenerator$print()

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Method new()

Creates a new instance of this R6 class.

Usage

TaskGeneratorCircle$new()

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Method plot()

Creates a simple plot of generated data.

Usage

TaskGeneratorCircle$plot(n = 200L, pch = 19L, ...)

Arguments

n

(integer(1))
Number of samples to draw for the plot. Default is 200.

pch

(integer(1))
Point char. Passed to plot().

...

(any)
Additional arguments passed to plot().

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Method clone()

The objects of this class are cloneable with this method.

Usage

TaskGeneratorCircle$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

generator = tgen("circle")
plot(generator, n = 200)


task = generator$generate(200)
str(task$data())
#> Classes ‘data.table’ and 'data.frame':	200 obs. of  3 variables:
#>  $ y : Factor w/ 2 levels "A","B": 2 1 2 2 2 1 2 1 1 1 ...
#>  $ x1: num  -0.86819 0.25554 0.00899 -0.49976 0.86006 ...
#>  $ x2: num  -0.545 0.528 0.843 -0.626 0.685 ...
#>  - attr(*, ".internal.selfref")=<externalptr>