최빈값 추정을 위한 의사결정나무

Abstract

Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied. Thus, we develop a new form of the modal decision tree model by integrating the kernel density estimation methods into the decision tree model. The simulation is conducted with four models. The results are compared for each size of the data when the predictor variable and the response variable are linear and nonlinear relationship cases. When the data has a linear relationship, the performance of the modal desicion tree model proposed in this paper is comparative to that of the previously proposed modal linear regression (MODLR) model. When the data has a nonlinear relationship, the performance of the modal tree model is better.