Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP
Abstract
:1. Introduction
2. Literature Review
2.1. Definition of Leisure Time
2.2. Micro-Influencing Factors of Leisure Time
2.3. Macro-Influencing Factors of Leisure Time
3. Methods
3.1. Light Gradient Boosting Machine (LightGBM)
3.2. SHapley Additive exPlanations (SHAP)
4. Data Preparation
4.1. Data Source and Processing
4.2. Variable Description
5. LightGBM Model Construction and Evaluation
5.1. Model Construction
- Step 1: Encoding the categorical variables. All categorical variables are encoded with integers as shown in Table 1. LightGBM can directly process categorical variables through special algorithms rather than using one-hot encoding.
- Step 2: Splitting the data set. Randomly split the data set into the train, validation, and test sets proportional to 8:1:1.
- Step 3: Training and optimizing. Train the model and optimize the parameters with five-fold cross-validation on the train set and validation set. The final parameters of LightGBM utilized in this paper are n_estimators = 270, num_leaves = 10, and learning_rate = 0.05.
- Step 4: Prediction and evaluation. Predict on the train set and test set and evaluate the model.
5.2. Evaluation Metrics
5.3. Model Evaluations
6. Analysis of the Changes and Influencing Factors of Leisure Time by Using SHAP
6.1. Changes in Beijing Residents’ Leisure Time over the Last 30 Years
6.2. Analysis on the Influencing Factors of Beijing Residents’ Leisure Time
6.2.1. Primary Factors Restricting Leisure Time: Work/Study and Housework
6.2.2. Age Differences and Gender Inequality in Leisure Time
6.2.3. Occupational Heterogeneity in Leisure Time
6.2.4. High Income and Caring for Others Squeezing Leisure Time
6.3. Interaction Effects of the Factors Influencing Beijing Residents’ Leisure Time
6.3.1. Gender Inequality Shifts over a Decade
6.3.2. Gender Inequality Shifts over the Educational Level
6.3.3. Leisure Time Changes for Family Caregivers over a Decade
6.3.4. Positive and Negative Effects of Weekly Rest Days
7. Conclusions and Discussions
7.1. Main Conclusions
7.2. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Class | Symbol | Meaning | Variable Type | Remarks |
---|---|---|---|---|
Dependent variable | leisure time | Residents’ leisure time | Categorical | 0: ≤median, 1: >median |
Year | year | Year | Categorical | 0: 2011, 1: 2016, 2: 2021 |
Demographic factors | gender | Gender | Categorical | 0: Male, 1: Female |
age | Age | Numerical | – | |
marital status | Marital status | Categorical | 0: Single, 1: Married | |
education | Educational level | Categorical | 0: Not
working, 1: In school; Years of education of current employees: 2: ≤9 years, 3: 9–12 years, 4: >12 years | |
weekly rest days | Weekly rest days | Categorical | 0: Not working, 1: Two days off per week, 2: Fewer than two days off per week | |
Occupational factors | enterprise ownership | Ownership of the work unit | Categorical | 0: Not working, 1: Enterprises owned by the whole people, 2: Collectively owned enterprises, 3: Individual industrial and commercial households, 4: Joint ventures, 5: Wholly owned enterprises, 6: Joint-stock enterprises, 7: Others |
occupation | occupational category | Categorical | 0: Not working, 1: Agriculture, forestry, animal husbandry, and fisheries, 2: Industrial and commercial services, 3: Professional technicians, 4: Workers or general staff, 5: Managers; 6: Literary artists, 7: Personal occupation, 8: Others | |
company size | Number of employees in the work unit | Categorical | 0: Not working, 1: Working in government agencies, 2: 1–29 employees, 3: 30–99 employees, 4: 100–499 employees, 5: ≥500 employees | |
Family factors | care or not | Is there anyone in the family who needs care | Categorical | 0: No, 1: Yes |
household income | Annual household income | Categorical | 0: <CNY 30,000; 1: CNY 30,000–50,000; 2: CNY 50,000–100,000; 3: CNY 100,000–200,000; 4: ≥CNY 200,000 | |
Time allocation factors | system time | Work/study time within the system | Numerical | – |
commuting time | Commuting time to work or to study in school | Numerical | – | |
essential time | Essential time for personal life | Numerical | – | |
housework time | Housework time | Numerical | – |
Predicted Value = Negative | Predicted Value = Positive | |
---|---|---|
Actual value = Negative | ||
Actual value = Positive |
Model | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|
LightGBM | 0.85 | 0.85 | 0.85 | 0.85 |
LR | 0.84 | 0.84 | 0.84 | 0.84 |
SVM | 0.84 | 0.84 | 0.84 | 0.84 |
RF | 0.82 | 0.82 | 0.81 | 0.81 |
DNN | 0.80 | 0.80 | 0.80 | 0.80 |
DT | 0.77 | 0.77 | 0.77 | 0.77 |
KNN | 0.74 | 0.74 | 0.73 | 0.73 |
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Wang, Q.; Jiang, Y. Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP. Mathematics 2023, 11, 2371. https://doi.org/10.3390/math11102371
Wang Q, Jiang Y. Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP. Mathematics. 2023; 11(10):2371. https://doi.org/10.3390/math11102371
Chicago/Turabian StyleWang, Qiyan, and Yuanyuan Jiang. 2023. "Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP" Mathematics 11, no. 10: 2371. https://doi.org/10.3390/math11102371
APA StyleWang, Q., & Jiang, Y. (2023). Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP. Mathematics, 11(10), 2371. https://doi.org/10.3390/math11102371