Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP
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 Bei...
Main Authors: | Qiyan Wang, Yuanyuan Jiang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-05-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/11/10/2371 |
Similar Items
-
Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models
by: Ioannis Karampinis, et al.
Published: (2024-07-01) -
A robust LightGBM model for concrete tensile strength forecast to aid in resilience-based structure strategies
by: Chukwuemeka Daniel
Published: (2024-10-01) -
LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses
by: Nitin Kumar Singh, et al.
Published: (2024-09-01) -
Fake news detection based on a hybrid BERT and LightGBM models
by: Ehab Essa, et al.
Published: (2023-05-01) -
Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model
by: Xiangqian Wang, et al.
Published: (2022-01-01)