Feature Selection for 'mlr3'
Rush Data Storage
Frozen Rush Data Storage
Class for Logging Evaluated Feature Sets
Assertions for Callbacks
Function for Automatic Feature Selection
Class for Automatic Feature Selection
Create Asynchronous Feature Selection Callback
Create Feature Selection Callback
Asynchronous Feature Selection Callback
Create Feature Selection Callback
Asynchronous Feature Selection Context
Evaluation Context
Embedded Ensemble Feature Selection
Ensemble Feature Selection Result
Wrapper-based Ensemble Feature Selection
Extract Inner Feature Selection Archives
Extract Inner Feature Selection Results
Fast Aggregation of ResampleResults and BenchmarkResults
Syntactic Sugar for Feature Selection Objects Construction
Function for Nested Resampling
Function for Feature Selection
Multi-Criteria Feature Selection with Rush
Single Criterion Feature Selection with Rush
Class for Multi Criteria Feature Selection
Class for Single Criterion Feature Selection
FSelector
Class for Asynchronous Feature Selection Algorithms
FSelectorAsyncFromOptimizerAsync
Class for Batch Feature Selection Algorithms
FSelectorBatchFromOptimizerBatch
Syntactic Sugar for Asynchronous Feature Selection Instance Constructi...
Syntactic Sugar for Feature Selection Instance Construction
Feature Selection with Asynchronous Design Points
Feature Selection with Asynchronous Exhaustive Search
Feature Selection with Asynchronous Random Search
Feature Selection with Design Points
Feature Selection with Exhaustive Search
Feature Selection with Genetic Search
Feature Selection with Random Search
Feature Selection with Recursive Feature Elimination
Feature Selection with Recursive Feature Elimination with Cross Valida...
Feature Selection with Sequential Search
Feature Selection with Shadow Variable Search
Dictionary of FSelectors
Assertion for mlr3fselect objects
mlr3fselect: Feature Selection for 'mlr3'
Freeze Archive Callback
Backup Benchmark Result Callback
Internal Tuning Callback
One Standard Error Rule Callback
SVM-RFE Callback
Class for Feature Selection Objective
Class for Feature Selection Objective
Class for Feature Selection Objective
Objects exported from other packages
Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.
Useful links