LearnerClust: Cluster Learner

In mlr3cluster: Cluster Extension for 'mlr3'

Cluster Learner

Description

This Learner specializes mlr3::Learner for cluster problems:

• task_type is set to "clust".

• Creates mlr3::Predictions of class PredictionClust.

• Possible values for predict_types are:

  • "partition": Integer indicating the cluster membership.

  • "prob": Probability for belonging to each cluster.

• Additional learner properties include:

  • "exclusive": The method natively assigns each observation to exactly one cluster.

  • "overlapping": The method natively assigns observations to multiple clusters.

  • "fuzzy": The method natively produces soft cluster memberships, e.g. fuzzy or probabilistic model-based methods. The hard partition is derived from the memberships.

  • "complete": Every observation is assigned to a cluster.

  • "partial": Observations may be left unassigned, e.g. as noise points.

  • "partitional": The method divides the data into non-nested clusters.

  • "hierarchical": The method produces a nested hierarchy of clusters.

  • "density": The method finds clusters as dense regions in the feature space.

  These properties describe the nature of the underlying method, not its interface capabilities: whether a learner can return soft memberships is encoded by the "prob" predict type, which "exclusive" learners may also support via derived scores.

Predefined learners can be found in the mlr3misc::Dictionary mlr3::mlr_learners.

Super class

mlr3::Learner -> LearnerClust

Public fields

assignments

(NULL | vector())
Cluster assignments from learned model.

save_assignments

(logical(1))
Should assignments for 'train' data be saved in the learner? Default is TRUE.

Methods

Public methods

• LearnerClust$new()

• LearnerClust$reset()

• LearnerClust$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

LearnerClust$new()

Creates a new instance of this R6 class.

Usage

LearnerClust$new(
  id,
  param_set = ps(),
  predict_types = "partition",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

predict_types

(character())
Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

feature_types

(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

properties

(character())
Set of properties of the mlr3::Learner. Must be a subset of mlr_reflections$learner_properties. The following properties are currently standardized and understood by learners in mlr3:

• "missings": The learner can handle missing values in the data.

• "weights": The learner supports observation weights.

• "offset": The learner can incorporate offset values to adjust predictions.

• "importance": The learner supports extraction of importance scores, i.e. comes with an ⁠$importance()⁠ extractor function (see section on optional extractors in mlr3::Learner).

• "selected_features": The learner supports extraction of the set of selected features, i.e. comes with a ⁠$selected_features()⁠ extractor function (see section on optional extractors in mlr3::Learner).

• "oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a oob_error() extractor function (see section on optional extractors in mlr3::Learner).

• "validation": The learner can use a validation task during training.

• "internal_tuning": The learner is able to internally optimize hyperparameters (those are also tagged with "internal_tuning").

• "marshal": To save learners with this property, you need to call ⁠$marshal()⁠ first. If a learner is in a marshaled state, you call first need to call ⁠$unmarshal()⁠ to use its model, e.g. for prediction.

• "hotstart_forward": The learner supports to hotstart a model forward.

• "hotstart_backward": The learner supports hotstarting a model backward.

• "featureless": The learner does not use features.

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.

---

LearnerClust$reset()

Reset assignments field before calling parent's reset().

Usage

LearnerClust$reset()

---

LearnerClust$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClust$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
library(mlr3cluster)
ids = mlr_learners$keys("^clust")
ids

# get a specific learner from mlr_learners:
learner = lrn("clust.kmeans")
print(learner)

mlr3cluster documentation built on June 11, 2026, 5:06 p.m.