원문정보
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목차
Ⅰ. Introduction
Ⅱ. Background
1. Transportation Extenditure and Vuilt Environment
2. Machine Learning in Transport Geography
1) Interpretable Machine Learning
2) Empirical Studies
3. Research Gaps and Contribution of this Paper
Ⅲ. Materials and Methods
1. Data
2. Variables
3. Machine Learning Approach
1) Extreme Gradient Boosting Decision Tree Regressor
2) Explainable AI
Ⅳ. Results
1. Ordinary Least Square Regression Model
2. Permutation-Based Feature Importance
3. SHAP Dependence Plots
Ⅴ. Discussion
1. Key Findings
2. Implications
3. Limitations
Ⅵ. Conclusions
참고문헌
Ⅱ. Background
1. Transportation Extenditure and Vuilt Environment
2. Machine Learning in Transport Geography
1) Interpretable Machine Learning
2) Empirical Studies
3. Research Gaps and Contribution of this Paper
Ⅲ. Materials and Methods
1. Data
2. Variables
3. Machine Learning Approach
1) Extreme Gradient Boosting Decision Tree Regressor
2) Explainable AI
Ⅳ. Results
1. Ordinary Least Square Regression Model
2. Permutation-Based Feature Importance
3. SHAP Dependence Plots
Ⅴ. Discussion
1. Key Findings
2. Implications
3. Limitations
Ⅵ. Conclusions
참고문헌
키워드
저자정보
참고문헌
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