mlr3fselect (version 0.3.0)

ObjectiveFSelect: ObjectiveFSelect

Description

Stores the objective function that estimates the performance of feature subsets. This class is usually constructed internally by by the FSelectInstanceSingleCrit / FSelectInstanceMultiCrit.

Super class

bbotk::Objective -> ObjectiveFSelect

Public fields

task

(mlr3::Task)

learner

(mlr3::Learner)

resampling

(mlr3::Resampling)

measures

(list of mlr3::Measure)

store_models

(logical(1)).

store_benchmark_result

(logical(1)).

archive

(ArchiveFSelect).

Methods

Public methods

• ObjectiveFSelect$new()

• ObjectiveFSelect$clone()

• bbotk::Objective$eval()

• bbotk::Objective$eval_dt()

• bbotk::Objective$eval_many()

• bbotk::Objective$format()

• bbotk::Objective$print()

Method new()

Creates a new instance of this R6 class.

Creates a new instance of this R6 class.

Usage

ObjectiveFSelect$new(
  task,
  learner,
  resampling,
  measures,
  check_values = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE
)

Arguments

task

(mlr3::Task) Task to operate on.

learner

(mlr3::Learner).

resampling

(mlr3::Resampling) Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits.

measures

(list of mlr3::Measure) Measures to optimize. If NULL, mlr3's default measure is used.

check_values

(logical(1)) Check the parameters before the evaluation and the results for validity?

store_benchmark_result

(logical(1)) Store benchmark result in archive?

store_models

(logical(1)). Store models in benchmark result?

Method clone()

The objects of this class are cloneable with this method.

Usage

ObjectiveFSelect$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.