mlr3 (version 0.18.0)

mlr_task_generators_2dnormals: 2D Normals Classification Task Generator

Description

A TaskGenerator for the 2d normals task in mlbench::mlbench.2dnormals().

Dictionary

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

mlr_task_generators$get("2dnormals")
tgen("2dnormals")

Parameters

Id	Type	Default	Range
cl	integer	-	\([2, \infty)\)
r	numeric	-	\([1, \infty)\)
sd	numeric	-	\([0, \infty)\)

Super class

mlr3::TaskGenerator -> TaskGenerator2DNormals

Methods

Public methods

• TaskGenerator2DNormals$new()

• TaskGenerator2DNormals$plot()

• TaskGenerator2DNormals$clone()

Inherited methods

• mlr3::TaskGenerator$format()
• mlr3::TaskGenerator$generate()
• mlr3::TaskGenerator$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

TaskGenerator2DNormals$new()

---

Method plot()

Creates a simple plot of generated data.

Usage

TaskGenerator2DNormals$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().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TaskGenerator2DNormals$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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, mlr_task_generators_cassini, mlr_task_generators_circle, mlr_task_generators_friedman1, mlr_task_generators_moons, mlr_task_generators_simplex, mlr_task_generators_smiley, mlr_task_generators_spirals, mlr_task_generators_xor

Examples

generator = tgen("2dnormals")
plot(generator, n = 200)

task = generator$generate(200)
str(task$data())