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One advantage of decision tree based methods like random forests is theirability to natively handle categorical predictors without having to firsttransform them (e.g., by using feature engineering techniques). However, inthis paper, we show how this capability can lead to an inherent "absent levels"problem for decision tree based methods that has never been thoroughlydiscussed, and whose consequences have never been carefully explored. Thisproblem occurs whenever there is an indeterminacy over how to handle anobservation that has reached a categorical split which was determined when theobservation in question's level was absent during training. Although theseincidents may appear to be innocuous, by using Leo Breiman and Adele Cutler'srandom forests FORTRAN code and the randomForest R package (Liaw and Wiener,2002) as motivating case studies, we examine how overlooking the absent levelsproblem can systematically bias a model. Furthermore, by using three real dataexamples, we illustrate how absent levels can dramatically alter a model'sperformance in practice, and we empirically demonstrate how some simpleheuristics can be used to help mitigate the effects of the absent levelsproblem until a more robust theoretical solution is found.

Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem