Abstract
Model selection consistency in the high{dimensional regression setting can be achieved only if strong assumptions are fulfilled. We therefore suggest to pursue a difierent goal, which we call a minimal class of models. The minimal class of models includes models that are similar in their prediction accuracy but not necessarily in their elements. We suggest a random search algorithm to reveal candidate models. The algorithm implements simulated annealing while using a score for each predictor that we suggest to derive using a combination of the lasso and the elastic net. The utility of using a minimal class of models is demonstrated in the analysis of two data sets.
Original language | English |
---|---|
Journal | Journal of Machine Learning Research |
Volume | 18 |
State | Published - 1 Apr 2017 |
Bibliographical note
Publisher Copyright:© 2017 Daniel Nevo and Ya'acov Ritov.
Keywords
- Elastic net
- High{dimensional data
- Lasso
- Model selection
- Simulated annealing