Random Forests are a convenient option for performing non-parametric regression. I will discuss a novel approach to error estimation using Random Forests; the relation of Random Forest regression to kernel regression, which offers a principled approach to configuration parameter selection resulting in lower regression error; and algorithmic considerations which yield asymptotically faster training than what is available in the de facto standard R implementation.
Speaker Biography
Samuel Carliles is a graduate student in the Department of Computer Science. He has a BS and an MS in Computer Science from Johns Hopkins, and currently works as a Data Scientist at AppNexus, Inc.