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      Exploring the predictors affecting the sense of community of Korean high school students: application of random forests and SHAP


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          Adolescence is a stage during which individuals develop social adaptability through meaningful interactions with others. During this period, students gradually expand their social networks outside the home, forming a sense of community. The aim of the current study was to explore the key predictors related to sense of community among Korean high school students and to develop supportive policies that enhance their sense of community. Accordingly, random forests and SHapley Additive exPlanations (SHAP) were applied to the 7th wave (11th graders) of the Korean Education Longitudinal Study 2013 data ( n = 6,077). As a result, 6 predictors positively associated with sense of community were identified, including self-related variables, “multicultural acceptance,” “behavioral regulation strategy,” and “peer attachment,” consistent with previous findings. Newly derived variables that predict sense of community include “positive recognition of volunteering,” “creativity,” “observance of rules” and “class attitude,” which are also positively related to sense of community. The implications of these results and some suggestions for future research are also discussed.

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              From local explanations to global understanding with explainable AI for trees

              Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are popular non-linear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here, we improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model’s performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains. Exact game-theoretic explanations for ensemble tree-based predictions that guarantee desirable properties.

                Author and article information

                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                06 February 2024
                : 15
                : 1337512
                Department of Education, Chungnam National University , Daejeon, Republic of Korea
                Author notes

                Edited by: Atsushi Oshio, Waseda University, Japan

                Reviewed by: Eka Miranda, Binus University, Indonesia; Celal Cakiroglu, University of Alberta, Canada

                *Correspondence: Hyewon Chung, hyewonchung7@ 123456gmail.com
                Copyright © 2024 Jang and Chung.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 14 November 2023
                : 17 January 2024
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 61, Pages: 11, Words: 7195
                Funded by: National Research Foundation of Korea, doi 10.13039/501100003725;
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2022M3J6A1084843).
                Brief Research Report
                Custom metadata
                Personality and Social Psychology

                Clinical Psychology & Psychiatry
                sense of community,machine learning,random forests,shapley additive explanations,korean high school students


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