An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data
Yufeng Ding, Jeffrey S. Simonoff; 11(6):131−170, 2010.
There are many different methods used by classification tree algorithms when missing data occur in the predictors, but few studies have been done comparing their appropriateness and performance. This paper provides both analytic and Monte Carlo evidence regarding the effectiveness of six popular missing data methods for classification trees applied to binary response data. We show that in the context of classification trees, the relationship between the missingness and the dependent variable, as well as the existence or non-existence of missing values in the testing data, are the most helpful criteria to distinguish different missing data methods. In particular, separate class is clearly the best method to use when the testing set has missing values and the missingness is related to the response variable. A real data set related to modeling bankruptcy of a firm is then analyzed. The paper concludes with discussion of adaptation of these results to logistic regression, and other potential generalizations.
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