Smoothing is a method of removing the rough (the error or noise component in data) and retaining the smooth (the predictable component in data) by averaging within “neighborhoods” of similar data values. Its utility is self-evident: No data analyst wants to model noise, producing a model that yields unreliable (large error variance) and inaccurate (large prediction bias) results. The purpose of this article is to discuss the concept behind smoothing, and then present CHAID as a data smoother. The illustration discussed is also found in - Ratner, B., Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, pages 74 – 84, CRC Press, Boca Raton, 2006.


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Dr. Bruce Ratner has explicated many novel and effective uses of CHAID ranging from statistical modeling and analysis to data mining.