Pattern-aided regression modeling and prediction model analysis

This paper first introduces a new style of regression models, namely pattern aided regression (PXR) models, aimed at representing accurate and interpretable prediction models. It also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models in an efficient manner. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by wide margins. From extensive experiments we also found that (1) regression modeling applications often involve complex diverse predictor-response relationships, which occur when the optimal regression models (of given regression model type) fitting distinct natural subgroups of data are highly different, and (2) state-of-the-art regression methods are often unable to adequately model such relationships. CPXR is also useful for analyzing how a given regression model makes prediction errors. This is an extended abstract of [6].