幸福感挖掘

在社会科学领域，幸福感的研究占有重要的位置。这个涉及了哲学、心理学、社会学、经济学等多方学科的话题复杂而有趣；同时与大家生活息息相关，每个人对幸福感都有自己的衡量标准。如果能发现影响幸福感的共性，生活中是不是将多一些乐趣；如果能找到影响幸福感的政策因素，便能优化资源配置来提升国民的幸福感。目前社会科学研究注重变量的可解释性和未来政策的落地，主要采用了线性回归和逻辑回归的方法，在收入、健康、职业、社交关系、休闲方式等经济人口因素；以及政府公共服务、宏观经济环境、税负等宏观因素上有了一系列的推测和发现。

赛题尝试了幸福感预测这一经典课题，希望在现有社会科学研究外有其他维度的算法尝试，结合多学科各自优势，挖掘潜在的影响因素，发现更多可解释、可理解的相关关系

In the field of social sciences, research on well-being occupies a pivotal position. This complex yet intriguing topic spans multiple disciplines including philosophy, psychology, sociology, economics and others, and is closely linked to people’s daily lives, as each individual has their own subjective metrics for measuring well-being. Uncovering the common factors influencing well-being could potentially bring more joy to people’s lives; meanwhile, identifying policy-related determinants of well-being would enable optimizing resource allocation to enhance national well-being.

Current social science research has focused on the interpretability of variables and the practical implementation of future policies, primarily adopting linear regression and logistic regression methodologies. It has yielded a series of inferences and findings regarding socioeconomic and demographic factors such as income, health, occupation, social relationships and leisure activities, as well as macro-level factors including government public services, macroeconomic environment and tax burden.

The competition task attempts to tackle this classic topic of well-being prediction, hoping to explore algorithmic approaches from other perspectives beyond existing social science research. By leveraging the respective strengths of multiple disciplines, it aims to excavate potential influencing factors and uncover more interpretable and understandable correlational relationships.

搜集汇总

背景与挑战

背景概述

该数据集基于《中国综合社会调查（CGSS）》的问卷调查数据，旨在通过机器学习方法预测幸福感，并探索个体、家庭及社会态度等多组变量之间的潜在关系。数据集侧重于社会科学研究中的可解释性分析，以挖掘影响幸福感的共性因素。