Feature selection is a fundamental challenge in high-dimensional regression, where identifying the most relevant variables is essential for both interpretability and predictive performance. Although LASSO is widely used for this purpose, its reliance on a single tuning parameter \(\lambda \) often limits its ability to recover the true active set. We address this issue by introducing a feature importance score, denoted \(I^*\) , that leverages the full LASSO path and aggregates information across multiple regularization levels. We develop two complementary algorithms for computing and applying this score: LASSO.PATH, which efficiently constructs \(I^*\) , and LASSO.PATH.BISEC, which selects variables by estimating an optimal data-driven threshold. We also propose the score-plot, a graphical tool that clearly separates relevant from irrelevant variables facilitating a more intuitive selection process. Through simulations and real data analyses, we demonstrate that our approach improves feature selection accuracy and reduces false discoveries compared with traditional LASSO-based methods.
