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Found 6 papers, 3 papers with code
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand.
S-LIME: Stabilized-LIME for Model Explanation
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare.
$V$-statistics and Variance Estimation
This paper develops a general framework for analyzing asymptotics of $V$-statistics.
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Furthermore, the paper provides a comprehensive comparison of student models with the benchmark model (decision tree) and the teacher model (gradient boosting trees) to quantify the fidelity and accuracy of the students' interpretations.
Unbiased Measurement of Feature Importance in Tree-Based Methods
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods.
Approximation Trees: Statistical Stability in Model Distillation
Here, we consider the use of regression trees as a student model, in which nodes of the tree can be used as `explanations' for particular predictions, and the whole structure of the tree can be used as a global representation of the resulting function.
