Keun Young Shin. 2020. Some Remarks on Gries’s Criticism on a Tree-based Approach of Multifactoral Data. Language and Information 24.1, 15-28. A tree model that is typically used in data mining and machine learning has been recently adopted to analyze multifactorial data in corpus linguistics. However, Gries (2019) claims that tree-based approaches fail to deal with a data set in which not all input variables are necessary for predicting the target variable accurately. He demonstrates that a tree model reports a three-way interaction even when a two-way interaction can predict the target variable with 100% accuracy because there is another third significant input variable. In this article, I will argue that the result of a conditional inference tree applied to such a data set suggests that a researcher should analyze a two-way interaction effect. In other words, the result allows two different interpretations that are related to the structure of a data set and requires to test whether a two-way interaction is sufficient to explain the data. In doing so, I will show that the presence of the third input variable does not hinder the detection of a perfect two-way interaction that is displayed by a symmetric tree figure. (Chonnam National University)