From 37254bb542e0d8a216ead78de2e6e1072ebb3450 Mon Sep 17 00:00:00 2001 From: bkoseoglu Date: Thu, 10 Oct 2024 09:04:54 +0200 Subject: [PATCH] add references to the paper --- README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 166d3ee..efde758 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ The basic idea is running a linear or logistic regression of the target on the Shapley values of the original features, on the validation set, discarding the features with negative coefficients, and ranking/filtering the rest according to their -statistical significance. For motivation and details, see the [example notebook](https://github.com/transferwise/shap-select/blob/main/docs/Quick%20feature%20selection%20through%20regression%20on%20Shapley%20values.ipynb) +statistical significance. For motivation and details, refer to our [research paper](https://arxiv.org/abs/2410.06815) see the [example notebook](https://github.com/transferwise/shap-select/blob/main/docs/Quick%20feature%20selection%20through%20regression%20on%20Shapley%20values.ipynb) Earlier packages using Shapley values for feature selection exist, the advantages of this one are * Regression on the **validation set** to combat overfitting @@ -109,3 +109,17 @@ selected_features_df = shap_select(model, X_val, y_val, task="multiclass", thres +## Citation + +If you use `shap-select` in your research, please cite our paper: + +```bibtex +@misc{kraev2024shapselectlightweightfeatureselection, + title={Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression}, + author={Egor Kraev and Baran Koseoglu and Luca Traverso and Mohammed Topiwalla}, + year={2024}, + eprint={2410.06815}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2410.06815}, +} \ No newline at end of file