Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Zhou, Zhengze"'
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determ
Externí odkaz:
http://arxiv.org/abs/2209.07980
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing eff
Externí odkaz:
http://arxiv.org/abs/2106.07875
Publikováno v:
In Journal of Transport Geography January 2024 114
This paper develops a general framework for analyzing asymptotics of $V$-statistics. Previous literature on limiting distribution mainly focuses on the cases when $n \to \infty$ with fixed kernel size $k$. Under some regularity conditions, we demonst
Externí odkaz:
http://arxiv.org/abs/1912.01089
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Machine learning has proved to be very successful for making predictions in travel behavior modeling. However, most machine-learning models have complex model structures and offer little or no explanation as to how they arrive at these predictions. I
Externí odkaz:
http://arxiv.org/abs/1910.13930
Autor:
Zhou, Zhengze, Hooker, Giles
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential sp
Externí odkaz:
http://arxiv.org/abs/1903.05179
This paper examines the stability of learned explanations for black-box predictions via model distillation with decision trees. One approach to intelligibility in machine learning is to use an understandable `student' model to mimic the output of an
Externí odkaz:
http://arxiv.org/abs/1808.07573
Publikováno v:
Data Mining & Knowledge Discovery; Sep2024, Vol. 38 Issue 5, p3308-3346, 39p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.