# Sparse Regression: Scalable algorithms and empirical performance

@article{Bertsimas2019SparseRS,
title={Sparse Regression: Scalable algorithms and empirical performance},
author={D. Bertsimas and J. Pauphilet and B. V. Parys},
journal={arXiv: Methodology},
year={2019}
}
• Published 2019
• Computer Science, Mathematics
• arXiv: Methodology
• In this paper, we review state-of-the-art methods for feature selection in statistics with an application-oriented eye. Indeed, sparsity is a valuable property and the profusion of research on the topic might have provided little guidance to practitioners. We demonstrate empirically how noise and correlation impact both the accuracy - the number of correct features selected - and the false detection - the number of incorrect features selected - for five methods: the cardinality-constrained… CONTINUE READING
22 Citations

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