A simple LearnerClust which assigns first n observations to cluster 1, second n observations to cluster 2, and so on. Hyperparameter num_clusters controls the number of clusters and is set to 1 by default. The train method tries to assign cluster memberships to each observation such that each cluster has an equal amount of observations. The predict method uses does the same thing as the train but for new data.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("clust.featureless")
lrn("clust.featureless")

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustFeatureless

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustFeatureless$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustFeatureless$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("clust.kmeans") print(learner)
#> <LearnerClustKMeans:clust.kmeans> #> * Model: - #> * Parameters: centers=2 #> * Packages: stats, clue #> * Predict Type: partition #> * Feature types: logical, integer, numeric #> * Properties: complete, exclusive, partitional
# available parameters: learner$param_set$ids()
#> [1] "centers" "iter.max" "algorithm" "nstart" "trace"