The goal of scientific inquiry is to uncover the principles that govern the world around us, and ultimately to express those principles in a mathematical form that reflects the empirical characteristics of observed data. In this regard, we have been exploring ways of modifying machine learning techniques so that the resulting predictive models likewise reflect the empirical characteristics of observed data. Following the principles of robust estimation, our methodology involves first examining the data to identify an appropriate family of statistical distributions for modeling the data, and then incorporating the corresponding maximum-likelihood estimation procedures into a decision tree algorithm. We have applied this methodology to insurance risk modeling and have obtained tree-based models superior to those obtained using conventional classification and regression tree algorithms.
By: Edwin P. D. Pednault, Chidanand Apte
Published in: RC21989 in 2001
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