by Jacob Si, Wendy Yusi Cheng*, Michael Cooper*, and Rahul G. Krishnan.
Figure 1: The InterpreTabNet Architecture.
Figure 2: Left (a): Learned masks associated with InterpreTabNet. Right (b): Learned masks associated with TabNet. Bottom (c): Stacked InterpreTabNet Feature Masks between subsequent feature masks.
Clone this repository and navigate to it in your terminal. Install required packages and dependencies as follows.
conda create -n interpretabnet python=3.10
conda activate interpretabnet
pip install -r requirements.txt
To run InterpreTabNet with a desired dataset, it is recommended to use the "interpretabnet.ipynb" file for the most up-to-date codebase.
Please consider citing our paper and giving it a 🌟 if you find it helpful. Thank you 😀!
@article{si2024interpretabnet,
title={InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation},
author={Si, Jacob and Cheng, Wendy Yusi and Cooper, Michael and Krishnan, Rahul G},
journal={arXiv preprint arXiv:2406.00426},
year={2024}
}