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mlr3fselect (development version)

mlr3fselect 1.0.0

CRAN release: 2024-06-29

  • feat: Add ensemble feature selection function ensemble_fselect().
  • BREAKING CHANGE: The FSelector class is FSelectorBatch now.
  • BREAKING CHANGE: THe FSelectInstanceSingleCrit and FSelectInstanceMultiCrit classes are FSelectInstanceBatchSingleCrit and FSelectInstanceBatchMultiCrit now.
  • BREAKING CHANGE: The CallbackFSelect class is CallbackBatchFSelect now.
  • BREAKING CHANGE: The ContextEval class is ContextBatchFSelect now.

mlr3fselect 0.12.0

CRAN release: 2024-03-09

  • feat: Add number of features to instance$result.
  • feat: Add ties_method options "least_features" and "random" to ArchiveBatchFSelect$best().
  • refactor: Optimize runtime of ArchiveBatchFSelect$best() method.
  • feat: Add importance scores to result of FSelectorRFE.
  • feat: Add number of features to as.data.table.ArchiveBatchFSelect().
  • feat: Features can be always included with the always_include column role.
  • fix: Add $phash() method to AutoFSelector.
  • fix: Include FSelector in hash of AutoFSelector.
  • refactor: Change default batch size of FSelectorBatchRandomSearch to 10.
  • feat: Add batch_size parameter to FSelectorBatchExhaustiveSearch to reduce memory consumption.
  • compatibility: Work with new paradox version 1.0.0

mlr3fselect 0.11.0

CRAN release: 2023-03-02

  • BREAKING CHANGE: The method parameter of fselect(), fselect_nested() and auto_fselector() is renamed to fselector. Only FSelector objects are accepted now. Arguments to the fselector cannot be passed with ... anymore.
  • BREAKING CHANGE: The fselect parameter of FSelector is moved to the first position to achieve consistency with the other functions.
  • docs: Update resources sections.
  • docs: Add list of default measures.

mlr3fselect 0.10.0

CRAN release: 2023-02-21

  • feat: Add callback mlr3fselect.svm_rfe to run recursive feature elimination on linear support vector machines.
  • refactor: The importance scores in FSelectorRFE are now aggregated by rank instead of averaging them.
  • feat: Add FSelectorRFECV optimizer to run recursive feature elimination with cross-validation.
  • refactor: FSelectorRFE works without store_models = TRUE now.
  • feat: The as.data.table.ArchiveBatchFSelect() function additionally returns a character vector of selected features for each row.
  • refactor: Add callbacks argument to fsi() function.

mlr3fselect 0.9.1

CRAN release: 2023-01-26

  • refactor: Remove internal use of mlr3pipelines.
  • fix: Feature selection with measures that require the importance or oob error works now.

mlr3fselect 0.9.0

CRAN release: 2022-12-21

  • fix: Add genalg to required packages of FSelectorBatchGeneticSearch.
  • feat: Add new callback that backups the benchmark result to disk after each batch.
  • feat: Create custom callbacks with the callback_batch_fselect() function.

mlr3fselect 0.8.0

CRAN release: 2022-11-16

  • refactor: FSelectorRFE throws an error if the learner does not support the $importance() method.
  • refactor: The AutoFSelector stores the instance and benchmark result if store_models = TRUE.
  • refactor: The AutoFSelector stores the instance if store_benchmark_result = TRUE.
  • feat: Add missing parameters from AutoFSelector to auto_fselect().
  • feat: Add fsi() function to create a FSelectInstanceBatchSingleCrit or FSelectInstanceBatchMultiCrit.
  • refactor: Remove unnest option from as.data.table.ArchiveBatchFSelect() function.

mlr3fselect 0.7.2

CRAN release: 2022-08-25

  • docs: Re-generate rd files with valid html.

mlr3fselect 0.7.1

CRAN release: 2022-05-03

  • feat: FSelector objects have the field $id now.

mlr3fselect 0.7.0

CRAN release: 2022-04-08

  • feat: Allow to pass FSelector objects as method in fselect() and auto_fselector().
  • feat: Added $label to FSelectors.
  • docs: New examples with fselect() function.
  • feat: $help() method which opens manual page of a FSelector.
  • feat: Added a as.data.table.DictionaryFSelector function.
  • feat: Added min_features parameter to FSelectorBatchSequential.

mlr3fselect 0.6.1

CRAN release: 2022-01-20

  • Add store_models flag to fselect().
  • Remove store_x_domain flag.

mlr3fselect 0.6.0

CRAN release: 2021-09-13

mlr3fselect 0.5.1

CRAN release: 2021-03-09

  • Remove x_domain column from archive.

mlr3fselect 0.5.0

CRAN release: 2021-01-24

  • FSelectorRFE stores importance values of each evaluated feature set in archive.
  • ArchiveBatchFSelect$data is a public field now.

mlr3fselect 0.4.1

CRAN release: 2020-10-30

  • Fix bug in AutoFSelector$predict()

mlr3fselect 0.4.0

CRAN release: 2020-10-22

  • Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
  • FSelectorRFE supports fraction of features to retain in each iteration (feature_fraction), number of features to remove in each iteration (feature_number) and vector of number of features to retain in each iteration (subset_sizes).
  • AutoFSelect is renamed to AutoFSelector.
  • To retrieve the inner feature selection results in nested resampling, as.data.table(rr)$learner[[1]]$fselect_result must be used now.
  • Option to control store_benchmark_result, store_models and check_values in AutoFSelector. store_fselect_instance must be set as a parameter during initialization.
  • Adds FSelectorBatchGeneticSearch.
  • Fixes check_values flag in FSelectInstanceBatchSingleCrit and FSelectInstanceBatchMultiCrit.
  • Removed dependency on orphaned package bibtex.
  • PipeOpSelect is internally used for task subsetting.

mlr3fselect 0.3.0

CRAN release: 2020-09-22

  • Archive is ArchiveBatchFSelect now which stores the benchmark result in $benchmark_result. This change removed the resample results from the archive but they can be still accessed via the benchmark result.

mlr3fselect 0.2.1

CRAN release: 2020-09-10

  • Warning message if external package for feature selection is not installed.

mlr3fselect 0.2.0

CRAN release: 2020-08-23

  • Initial CRAN release.