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Cluster Measure

Source: R/MeasureClust.R
MeasureClust.Rd

This measure specializes mlr3::Measure for cluster analysis:

  • task_type is set to "clust".

  • Possible values for predict_type are "partition" and "prob".

Predefined measures can be found in the mlr3misc::Dictionary mlr3::mlr_measures.

See also

Example cluster measures: clust.dunn

Super class

mlr3::Measure -> MeasureClust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureClust$new(
  id,
  range,
  minimize = NA,
  aggregator = NULL,
  properties = character(),
  predict_type = "partition",
  task_properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

id

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

range

(numeric(2))
Feasible range for this measure as c(lower_bound, upper_bound). Both bounds may be infinite.

minimize

(logical(1))
Set to TRUE if good predictions correspond to small values, and to FALSE if good predictions correspond to large values. If set to NA (default), tuning this measure is not possible.

aggregator

(function() | NULL)
Function to aggregate over multiple iterations. The role of this function depends on the value of field "average":

  • "macro": A numeric vector of scores (one per iteration) is passed. The aggregate function defaults to mean() in this case.

  • "micro": The aggregator function is not used. Instead, predictions from multiple iterations are first combined and then scored in one go.

  • "custom": A mlr3::ResampleResult is passed to the aggregate function.

properties

(character())
Properties of the measure. Must be a subset of mlr_reflections$measure_properties. Supported by mlr3:

  • "requires_task" (requires the complete mlr3::Task),

  • "requires_learner" (requires the trained mlr3::Learner),

  • "requires_model" (requires the trained mlr3::Learner, including the fitted model),

  • "requires_train_set" (requires the training indices from the mlr3::Resampling),

  • "na_score" (the measure is expected to occasionally return NA or NaN),

  • "weights" (support weighted scoring using sample weights from task, column role weights_measure),

  • "primary_iters" (the measure explicitly handles resamplings that only use a subset of their iterations for the point estimate), and

  • "requires_no_prediction" (No prediction is required; This usually means that the measure extracts some information from the learner state.).

predict_type

(character(1))
Required predict type of the mlr3::Learner. Possible values are stored in mlr_reflections$learner_predict_types.

task_properties

(character())
Required task properties, see mlr3::Task.

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().