mlr3filters (version 0.3.0)

mlr_filters_performance: Predictive Performance Filter

Description

Filter which uses the predictive performance of a mlr3::Learner as filter score. Performs a mlr3::resample() for each feature separately. The filter score is the aggregated performance of the mlr3::Measure, or the negated aggregated performance if the measure has to be minimized.

Super class

mlr3filters::Filter -> FilterPerformance

Public fields

learner

(mlr3::Learner)

resampling

(mlr3::Resampling)

measure

(mlr3::Measure)

Methods

Public methods

• FilterPerformance$new()

• FilterPerformance$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterDISR object.

Usage

FilterPerformance$new(
  id = "performance",
  task_type = learner$task_type,
  param_set = learner$param_set,
  feature_types = learner$feature_types,
  learner = mlr3::lrn("classif.rpart"),
  resampling = mlr3::rsmp("holdout"),
  measure = mlr3::msr("classif.ce")
)

Arguments

id

(character(1)) Identifier for the filter.

task_type

(character()) Types of the task the filter can operator on. E.g., "classif" or "regr".

param_set

(paradox::ParamSet) Set of hyperparameters.

feature_types

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

learner

(mlr3::Learner) mlr3::Learner to use for model fitting.

resampling

(mlr3::Resampling) mlr3::Resampling to be used within resampling.

measure

(mlr3::Measure) mlr3::Measure to be used for evaluating the performance.

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterPerformance$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, mlr_filters_carscore, mlr_filters_cmim, mlr_filters_correlation, mlr_filters_disr, mlr_filters_find_correlation, mlr_filters_importance, mlr_filters_information_gain, mlr_filters_jmim, mlr_filters_jmi, mlr_filters_kruskal_test, mlr_filters_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_permutation, mlr_filters_variance, mlr_filters

Examples

# NOT RUN {
task = mlr3::tsk("iris")
learner = mlr3::lrn("classif.rpart")
filter = flt("performance", learner = learner)
filter$calculate(task)
as.data.table(filter)
# }