Detecting and Interpreting Variable Interactions in Observational Ornithology Data

Abstract

In this paper we demonstrate a practical approach to interaction detection on real data describing the abundance of different species of birds in the prairies east of the southern Rocky Mountains. This data is very noisy---predictive models built from it perform only slightly better than baseline. Previous approaches for interaction detection, including a recently proposed algorithm based on Additive Groves, often do not work well on such noisy data for a number of reasons. We describe the issues that appear when working with such data sets and suggest solutions to them. In the end, we discuss results of our analysis for several bird species.