SWAG: A Wrapper Method for Sparse Learning

no code implementations • 23 Jun 2020 • Roberto Molinari, Gaetan Bakalli, Stéphane Guerrier, Cesare Miglioli, Samuel Orso, Mucyo Karemera, Olivier Scaillet

As a consequence, there is the need to make the outputs of machine learning algorithms more interpretable and to deliver a library of "equivalent" learners (in terms of prediction performance) that users can select based on attribute availability in order to test and/or make use of these learners for predictive/diagnostic purposes.

Attribute Sparse Learning