Mlr3fselect: Feature Selection for 'Mlr3'

MCML Authors

Marc Becker

→ Group Bernd Bischl
 Statistical Learning and Data Science

Patrick Schratz

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Michel Lang

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

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.

manual BSL+20

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SW Package

Jan. 2020

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Authors

M. Becker • P. Schratz • M. Lang • B. Bischl

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Links

URL

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Research Area

 A1 | Statistical Foundations & Explainability