Package: mlr3filters 0.8.0
Michel Lang
mlr3filters: Filter Based Feature Selection for 'mlr3'
Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.
Authors:
mlr3filters_0.8.0.tar.gz
mlr3filters_0.8.0.zip(r-4.5)mlr3filters_0.8.0.zip(r-4.4)mlr3filters_0.8.0.zip(r-4.3)
mlr3filters_0.8.0.tgz(r-4.4-any)mlr3filters_0.8.0.tgz(r-4.3-any)
mlr3filters_0.8.0.tar.gz(r-4.5-noble)mlr3filters_0.8.0.tar.gz(r-4.4-noble)
mlr3filters_0.8.0.tgz(r-4.4-emscripten)mlr3filters_0.8.0.tgz(r-4.3-emscripten)
mlr3filters.pdf |mlr3filters.html✨
mlr3filters/json (API)
NEWS
| # Install 'mlr3filters' in R: |
| install.packages('mlr3filters', repos = c('https://mlr-org.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlr-org/mlr3filters/issues
feature-selectionfilterfiltersmlrmlr3variable-importance
Last updated 5 months agofrom:2fd6333e6c (on v0.8.0). Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 20 2024 |
| R-4.5-win | OK | Aug 20 2024 |
| R-4.5-linux | OK | Aug 20 2024 |
| R-4.4-win | OK | Aug 20 2024 |
| R-4.4-mac | OK | Aug 20 2024 |
| R-4.3-win | OK | Aug 20 2024 |
| R-4.3-mac | OK | Aug 20 2024 |
Exports:as.data.tableFilterFilterAnovaFilterAUCFilterBorutaFilterCarScoreFilterCarSurvScoreFilterCMIMFilterCorrelationFilterDISRFilterFindCorrelationFilterImportanceFilterInformationGainFilterJMIFilterJMIMFilterKruskalTestFilterMIMFilterMRMRFilterNJMIMFilterPerformanceFilterPermutationFilterReliefFilterSelectedFeaturesFilterUnivariateCoxFilterVariancefltfltsmlr_filters
Dependencies:backportscheckmatecodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslgrlistenvmlbenchmlr3mlr3measuresmlr3miscpalmerpenguinsparadoxparallellyPRROCR6uuid
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| mlr3filters: Filter Based Feature Selection for 'mlr3' | mlr3filters-package mlr3filters |
| Filter Base Class | Filter |
| Syntactic Sugar for Filter Construction | flt flts |
| Dictionary of Filters | mlr_filters |
| ANOVA F-Test Filter | FilterAnova mlr_filters_anova |
| AUC Filter | FilterAUC mlr_filters_auc |
| Burota Filter | FilterBoruta mlr_filters_boruta |
| Correlation-Adjusted Marignal Correlation Score Filter | FilterCarScore mlr_filters_carscore |
| Correlation-Adjusted Survival Score Filter | FilterCarSurvScore mlr_filters_carsurvscore |
| Minimal Conditional Mutual Information Maximization Filter | FilterCMIM mlr_filters_cmim |
| Correlation Filter | FilterCorrelation mlr_filters_correlation |
| Double Input Symmetrical Relevance Filter | FilterDISR mlr_filters_disr |
| Correlation Filter | FilterFindCorrelation mlr_filters_find_correlation |
| Filter for Embedded Feature Selection via Variable Importance | FilterImportance mlr_filters_importance |
| Information Gain Filter | FilterInformationGain mlr_filters_information_gain |
| Joint Mutual Information Filter | FilterJMI mlr_filters_jmi |
| Minimal Joint Mutual Information Maximization Filter | FilterJMIM mlr_filters_jmim |
| Kruskal-Wallis Test Filter | FilterKruskalTest mlr_filters_kruskal_test |
| Mutual Information Maximization Filter | FilterMIM mlr_filters_mim |
| Minimum Redundancy Maximal Relevancy Filter | FilterMRMR mlr_filters_mrmr |
| Minimal Normalised Joint Mutual Information Maximization Filter | FilterNJMIM mlr_filters_njmim |
| Predictive Performance Filter | FilterPerformance mlr_filters_performance |
| Permutation Score Filter | FilterPermutation mlr_filters_permutation |
| RELIEF Filter | FilterRelief mlr_filters_relief |
| Filter for Embedded Feature Selection | FilterSelectedFeatures mlr_filters_selected_features |
| Univariate Cox Survival Filter | FilterUnivariateCox mlr_filters_univariate_cox |
| Variance Filter | FilterVariance mlr_filters_variance |
