Abstract
The tree based method is a conventional statistical method that involves constructing a tree structure for a classification model through recursively splitting a dataset by explanatory variables to minimize some impurity criteria for the response variable. This tree structure induces many if-then rules with product forms. In this paper, we study a basic tree based approach — the classification and regression trees (CART) method — based on a simulation model for data generation and verification for induced rules. We compare CART with the statistical test rule induction method (STRIM) to clarify its performance and problems. We also apply both methods to a real-world dataset and consider their performances based on the simulation results.
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We truly thank Rakuten Inc. for presenting Rakuten Travel dataset [19].
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Kato, Y., Kawaguchi, S., Saeki, T. (2018). Studies on CART’s Performance in Rule Induction and Comparisons by STRIM. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_12
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