On the Oracle Properties of Bayesian Random Forest for Sparsed High-Dimensional Gaussian Regression
Oyebayo Ridwan Olaniran, Ali Alzahrani · Preprints.org · 2023
Random Forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian Random Forest (BRF) method specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising efficiency or bias.