From ecac0ac1e83888748e97c0a4188259c763655f90 Mon Sep 17 00:00:00 2001
From: "Lisa B. A. Sousa" <44869855+lisa-sousa@users.noreply.github.com>
Date: Thu, 16 May 2024 13:07:12 +0200
Subject: [PATCH] Update shap.rst

---
 docs/source/_model_agnostic_xai/shap.rst | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/source/_model_agnostic_xai/shap.rst b/docs/source/_model_agnostic_xai/shap.rst
index 023c31a..5810bf7 100644
--- a/docs/source/_model_agnostic_xai/shap.rst
+++ b/docs/source/_model_agnostic_xai/shap.rst
@@ -21,10 +21,10 @@ SHAP provides KernelSHAP, an alternative, kernel-based estimation approach for S
 References
 -----------
 
-- Original SHAP paper: Lundberg, S. M., & Lee, S. I. `A unified approach to interpreting model predictions. <https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html>`_ NeurIPS. 2017
-- Intro to TreeExplainer: Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. `From local explanations to global understanding with explainable AI for trees. <https://doi.org/10.1038/s42256-019-0138-9>`_ Nature machine intelligence. 2020.
-- Intro to TreeExplainer accelerated with GPUs: Mitchell, R., Frank, E., & Holmes, G. `GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. <https://doi.org/10.48550/arXiv.2010.13972>`_ arxiv. 2022
-- Intro to Integrated Gradients: Sundararajan, M., Taly, A., & Yan, Q. `Axiomatic attribution for deep networks. <https://doi.org/10.48550/arXiv.1703.01365>`_ PMLR. 2017.
-- Visualizing the Impact of Feature Attribution Baselines: `blog post <https://distill.pub/2020/attribution-baselines/>`_
-- XAI Book with focus on SHAP: Molnar, C. `Interpreting Machine Learning Models With SHAP. <https://leanpub.com/shap>`_ 2022
-- XAI Book: Molnar, C. `Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. <https://christophm.github.io/interpretable-ml-book/>`_ Lulu.com. 2022.
+- **Original SHAP paper:** Lundberg, S. M., & Lee, S. I. `A unified approach to interpreting model predictions. <https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html>`_ NeurIPS. 2017
+- **Intro to TreeExplainer:** Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. `From local explanations to global understanding with explainable AI for trees. <https://doi.org/10.1038/s42256-019-0138-9>`_ Nature machine intelligence. 2020.
+- **Intro to TreeExplainer accelerated with GPUs:** Mitchell, R., Frank, E., & Holmes, G. `GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. <https://doi.org/10.48550/arXiv.2010.13972>`_ arxiv. 2022
+- **Intro to Integrated Gradients:** Sundararajan, M., Taly, A., & Yan, Q. `Axiomatic attribution for deep networks. <https://doi.org/10.48550/arXiv.1703.01365>`_ PMLR. 2017.
+- **Visualizing the Impact of Feature Attribution Baselines:** `blog post <https://distill.pub/2020/attribution-baselines/>`_
+- **XAI Book with focus on SHAP:** Molnar, C. `Interpreting Machine Learning Models With SHAP. <https://leanpub.com/shap>`_ 2022
+- **XAI Book:** Molnar, C. `Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. <https://christophm.github.io/interpretable-ml-book/>`_ Lulu.com. 2022.