Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP

Qiyan Wang and Yuanyuan Jiang ()
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Qiyan Wang: Leisure Economy Research Center, Renmin University of China, Beijing 100872, China
Yuanyuan Jiang: School of Statistics, Renmin University of China, Beijing 100872, China

Mathematics, 2023, vol. 11, issue 10, 1-22

Abstract: Leisure time is crucial for personal development and leisure consumption. Accurate prediction of leisure time and analysis of its influencing factors creates a benefit by increasing personal leisure time. We predict leisure time and analyze its key influencing factors according to survey data of Beijing residents’ time allocation in 2011, 2016, and 2021, with an effective sample size of 3356. A Light Gradient Boosting Machine (LightGBM) model is utilized to classify and predict leisure time, and the SHapley Additive exPlanation (SHAP) approach is utilized to conduct feature importance analysis and influence mechanism analysis of influencing factors from four perspectives: time allocation, demographics, occupation, and family characteristics. The results verify that LightGBM effectively predicts personal leisure time, with the test set’s accuracy, recall, and F1 values being 0.85 and the AUC value reaching 0.91. The results of SHAP highlight that work/study time within the system is the main constraint on leisure time. Demographic factors, such as gender and age, are also of great significance for leisure time. Occupational and family heterogeneity exist in leisure time as well. The results contribute to the government improving the public holiday system, companies designing personalized leisure products for users with different leisure characteristics, and residents understanding and independently increasing their leisure time.

Keywords: data analysis; classification; decision trees; LightGBM; SHAP; leisure time; influencing factors; time allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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