Best Subset

Best subset selection aims to identify the optimal subset of features from a larger set that best explains a given outcome, a crucial task in various fields including machine learning and statistics. Current research focuses on developing computationally efficient algorithms, particularly for high-dimensional data, employing techniques like primal-dual iterations, greedy selection mechanisms, and splicing methods to improve speed and accuracy while maintaining statistical guarantees. These advancements are significant for improving model interpretability, reducing computational costs in large-scale data analysis, and enabling more effective decision-making across diverse applications.