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README.txt
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README.txt
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This repository contains the Python code for training and evaluating a two-layer additive risk model on the Home Equity Line of Credit (HELOC) dataset used in the 2018 Explainable Machine Learning Challenge (https://community.fico.com/s/explainable-machine-learning-challenge). Our team (Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, and Tong Wang) won the FICO Recognition Award (https://www.fico.com/en/newsroom/fico-announces-winners-inaugural-xml-challenge).
If you plan to use our code, please cite our paper:
@article{chen2022holistic,
title={A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations},
author={Chen, Chaofan and Lin, Kangcheng and Rudin, Cynthia and Shaposhnik, Yaron and Wang, Sijia and Wang, Tong},
journal={Decision Support Systems},
volume={152},
pages={113647},
year={2022},
publisher={Elsevier}
}
You may find our paper here:
https://www.sciencedirect.com/science/article/abs/pii/S0167923621001573
If you would like to try out a trained model on a browser, you may use the following website that provides an interactive visualization of our model:
https://dukedatasciencefico.cs.duke.edu/models/