Optimal Sparse Recovery with Decision Stumps

Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often in practice feature selection, theoretical guarantees these methods not well understood. We here obtain a tight finite sample bound selection problem linear regression using single-depth decision trees. examine statistical properties "decision stumps" recovery s active features from p total features, where