Simplex Classification Task Generator
Source:R/TaskGeneratorSimplex.R
mlr_task_generators_simplex.Rd
A TaskGenerator for the simplex task in mlbench::mlbench.simplex()
.
Note that the generator implemented in mlbench returns fewer samples than requested.
Dictionary
This TaskGenerator can be instantiated via the dictionary mlr_task_generators or with the associated sugar function tgen()
:
Parameters
Id | Type | Default | Levels | Range |
center | logical | TRUE | TRUE, FALSE | - |
d | integer | 3 | \([1, \infty)\) | |
sd | numeric | 0.1 | \([0, \infty)\) | |
sides | integer | 1 | \([1, \infty)\) |
See also
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_2dnormals
,
mlr_task_generators_cassini
,
mlr_task_generators_circle
,
mlr_task_generators_friedman1
,
mlr_task_generators_moons
,
mlr_task_generators_smiley
,
mlr_task_generators_spirals
,
mlr_task_generators_xor
Super class
mlr3::TaskGenerator
-> TaskGeneratorSimplex
Examples
generator = tgen("simplex")
plot(generator, n = 200)
task = generator$generate(200)
str(task$data())
#> Classes ‘data.table’ and 'data.frame': 100 obs. of 4 variables:
#> $ y : Factor w/ 4 levels "A","B","C","D": 1 1 1 1 1 1 1 1 1 1 ...
#> $ x1: num -0.497 -0.513 -0.524 -0.46 -0.461 ...
#> $ x2: num -0.281 -0.13 -0.326 -0.253 -0.264 ...
#> $ x3: num -0.0298 -0.1385 -0.2666 -0.3657 -0.2486 ...
#> - attr(*, ".internal.selfref")=<externalptr>