Knockoff Boosted Tree for Model-Free Variable Selection

Understand

In this article, we propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models.

• Our method is inspired by the original knockoff method, where the differences between original and knockoff variables are used for variable selection with false discovery rate control.
• The original method uses Lasso for regression models and assumes there are more samples than variables.
• We extend this method to both model-free and high-dimensional variable selection.