no code implementations • 5 Sep 2023 • Linwei Hu, Soroush Aramideh, Jie Chen, Vijayan N. Nair
It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost.
no code implementations • 5 Sep 2023 • Linwei Hu, Ke Wang
Finally, if even the order of model is unknown, we propose an iterative way to approximate Shapley values.
no code implementations • 25 May 2023 • Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang, Jie Chen
To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations.
no code implementations • 14 Jul 2022 • Linwei Hu, Jie Chen, Vijayan N. Nair
We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance.
no code implementations • 11 Jul 2022 • Zhipu Zhou, Jie Chen, Linwei Hu
Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties.
no code implementations • 27 Apr 2022 • Alice J. Liu, Arpita Mukherjee, Linwei Hu, Jie Chen, Vijayan N. Nair
Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models.
no code implementations • 26 Apr 2022 • Vijayan N. Nair, Tianshu Feng, Linwei Hu, Zach Zhang, Jie Chen, Agus Sudjianto
The applicant is then entitled to an explanation for the negative decision.
no code implementations • 28 Jul 2020 • Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods.
no code implementations • 28 Jul 2020 • Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
no code implementations • 2 Jun 2018 • Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
This is in contrast with the KLIME approach that is based on clustering the predictor space.