scipy-conference · fwkoch · Oct 14, 2025 · May 29, 2025 · Jun 4, 2025 · Jun 4, 2025
diff --git a/papers/avik_basu/banner.png b/papers/avik_basu/banner.png
diff --git a/papers/avik_basu/beeswarm_Laying_cnn.png b/papers/avik_basu/beeswarm_Laying_cnn.png
diff --git a/papers/avik_basu/beeswarm_Sitting_cnn.png b/papers/avik_basu/beeswarm_Sitting_cnn.png
diff --git a/papers/avik_basu/beeswarm_Standing_cnn.png b/papers/avik_basu/beeswarm_Standing_cnn.png
diff --git a/papers/avik_basu/beeswarm_Walking_Downstairs_cnn.png b/papers/avik_basu/beeswarm_Walking_Downstairs_cnn.png
diff --git a/papers/avik_basu/beeswarm_Walking_Upstairs_cnn.png b/papers/avik_basu/beeswarm_Walking_Upstairs_cnn.png
diff --git a/papers/avik_basu/beeswarm_Walking_cnn.png b/papers/avik_basu/beeswarm_Walking_cnn.png
diff --git a/papers/avik_basu/beeswarm_avg_cnn.png b/papers/avik_basu/beeswarm_avg_cnn.png
diff --git a/papers/avik_basu/beeswarm_xgb.png b/papers/avik_basu/beeswarm_xgb.png
diff --git a/papers/avik_basu/dep_Laying_cnn.png b/papers/avik_basu/dep_Laying_cnn.png
diff --git a/papers/avik_basu/dep_Sitting_cnn.png b/papers/avik_basu/dep_Sitting_cnn.png
diff --git a/papers/avik_basu/dep_Standing_cnn.png b/papers/avik_basu/dep_Standing_cnn.png
diff --git a/papers/avik_basu/dep_Walking_Downstairs_cnn.png b/papers/avik_basu/dep_Walking_Downstairs_cnn.png
diff --git a/papers/avik_basu/dep_Walking_Upstairs_cnn.png b/papers/avik_basu/dep_Walking_Upstairs_cnn.png
diff --git a/papers/avik_basu/dep_Walking_cnn.png b/papers/avik_basu/dep_Walking_cnn.png
diff --git a/papers/avik_basu/dep_categorical_xgb.png b/papers/avik_basu/dep_categorical_xgb.png
diff --git a/papers/avik_basu/dep_contact_xgb.png b/papers/avik_basu/dep_contact_xgb.png
diff --git a/papers/avik_basu/dep_poutcome_xgb.png b/papers/avik_basu/dep_poutcome_xgb.png
diff --git a/papers/avik_basu/local_Laying_cnn.png b/papers/avik_basu/local_Laying_cnn.png
diff --git a/papers/avik_basu/local_Sitting_cnn.png b/papers/avik_basu/local_Sitting_cnn.png
diff --git a/papers/avik_basu/local_Standing_cnn.png b/papers/avik_basu/local_Standing_cnn.png
diff --git a/papers/avik_basu/local_Walking_Downstairs_cnn.png b/papers/avik_basu/local_Walking_Downstairs_cnn.png
diff --git a/papers/avik_basu/local_Walking_Upstairs_cnn.png b/papers/avik_basu/local_Walking_Upstairs_cnn.png
diff --git a/papers/avik_basu/local_Walking_cnn.png b/papers/avik_basu/local_Walking_cnn.png
diff --git a/papers/avik_basu/main.md b/papers/avik_basu/main.md
diff --git a/papers/avik_basu/mybib.bib b/papers/avik_basu/mybib.bib
@@ -0,0 +1,296 @@
+@inproceedings{jupyter,
+  abstract  = {It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.},
+  author    = {Kluyver, Thomas and Ragan-Kelley, Benjamin and Pérez, Fernando and Granger, Brian and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and Ivanov, Paul and Avila, Damián and Abdalla, Safia and Willing, Carol and {Jupyter development team}},
+  editor    = {Loizides, Fernando and Scmidt, Birgit},
+  location  = {Netherlands},
+  publisher = {IOS Press},
+  url       = {https://eprints.soton.ac.uk/403913/},
+  booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas},
+  year      = {2016},
+  pages     = {87--90},
+  title     = {Jupyter Notebooks - a publishing format for reproducible computational workflows},
+}
+
+@article{matplotlib,
+  abstract     = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.},
+  author       = {Hunter, J. D.},
+  publisher    = {IEEE COMPUTER SOC},
+  year         = {2007},
+  doi          = {https://doi.org/10.1109/MCSE.2007.55},
+  journal      = {Computing in Science \& Engineering},
+  number       = {3},
+  pages        = {90--95},
+  title        = {Matplotlib: A 2D graphics environment},
+  volume       = {9},
+}
+
+@article{numpy,
+  author       = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, Stéfan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del Río, Jaime Fernández and Wiebe, Mark and Peterson, Pearu and Gérard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
+  publisher    = {Springer Science and Business Media {LLC}},
+  doi          = {https://doi.org/10.1038/s41586-020-2649-2},
+  date         = {2020-09},
+  year         = {2020},
+  journal      = {Nature},
+  number       = {7825},
+  pages        = {357--362},
+  title        = {Array programming with {NumPy}},
+  volume       = {585},
+}
+
+@misc{pandas1,
+  author    = {{The Pandas Development Team}},
+  title     = {pandas-dev/pandas: Pandas},
+  month     = feb,
+  year      = {2020},
+  publisher = {Zenodo},
+  version   = {latest},
+  url       = {https://doi.org/10.5281/zenodo.3509134},
+}
+
+@inproceedings{pandas2,
+  author    = {Wes McKinney},
+  title     = {{D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython},
+  booktitle = {{P}roceedings of the 9th {P}ython in {S}cience {C}onference},
+  pages     = {56 - 61},
+  year      = {2010},
+  editor    = {{S}t\'efan van der {W}alt and {J}arrod {M}illman},
+  doi       = {https://doi.org/10.25080/Majora-92bf1922-00a},
+}
+
+@article{scipy,
+  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
+            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
+            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
+            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
+            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
+            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
+            Kern, Robert and Larson, Eric and Carey, C J and
+            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
+            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
+            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
+            Harris, Charles R. and Archibald, Anne M. and
+            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
+            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
+  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
+            Computing in Python}},
+  journal = {Nature Methods},
+  year    = {2020},
+  volume  = {17},
+  pages   = {261--272},
+  adsurl  = {https://rdcu.be/b08Wh},
+  doi     = {https://doi.org/10.1038/s41592-019-0686-2},
+}
+
+@article{sklearn1,
+  author       = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+  year         = {2011},
+  journal      = {Journal of Machine Learning Research},
+  pages        = {2825--2830},
+  title        = {Scikit-learn: Machine Learning in {P}ython},
+  volume       = {12},
+}
+
+@inproceedings{sklearn2,
+  author    = {Buitinck, Lars and Louppe, Gilles and Blondel, Mathieu and Pedregosa, Fabian and Mueller, Andreas and Grisel, Olivier and Niculae, Vlad and Prettenhofer, Peter and Gramfort, Alexandre and Grobler, Jaques and Layton, Robert and VanderPlas, Jake and Joly, Arnaud and Holt, Brian and Varoquaux, Gaël},
+  booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
+  year      = {2013},
+  pages     = {108--122},
+  title     = {{API} design for machine learning software: experiences from the scikit-learn project},
+}
+
+@misc{human_activity_recognition_using_smartphones_240,
+  author       = {Reyes-Ortiz, Jorge, Anguita, Davide, Ghio, Alessandro, Oneto, Luca, and Parra, Xavier},
+  title        = {{Human Activity Recognition Using Smartphones}},
+  year         = {2013},
+  howpublished = {UCI Machine Learning Repository},
+  doi          = {https://doi.org/10.24432/C54S4K},
+  note         = {{DOI}: https://doi.org/10.24432/C54S4K}
+}
+
+@misc{bank_marketing_222,
+  author       = {Moro, S., Rita, P., and Cortez, P.},
+  title        = {{Bank Marketing}},
+  year         = {2014},
+  howpublished = {UCI Machine Learning Repository},
+  doi          = {https://doi.org/10.24432/C5K306},
+  note         = {{DOI}: https://doi.org/10.24432/C5K306}
+}
+
+
+@inproceedings{DBLP:journals/corr/ChenG16,
+  author    = {Tianqi Chen and Carlos Guestrin},
+  title     = {XGBoost: A Scalable Tree Boosting System},
+  booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+  year      = {2016},
+  pages     = {785--794},
+  doi       = {https://doi.org/10.1145/2939672.2939785},
+}
+
+@misc{paszke2019pytorchimperativestylehighperformance,
+      title={PyTorch: An Imperative Style, High-Performance Deep Learning Library},
+      author={Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas Köpf and Edward Yang and Zach DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala},
+      year={2019},
+      eprint={1912.01703},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1912.01703},
+      doi = {https://doi.org/10.48550/arXiv.1912.01703},
+}
+
+@article{DBLP:journals/corr/LundbergL17,
+  author       = {Scott M. Lundberg and
+                  Su{-}In Lee},
+  title        = {A unified approach to interpreting model predictions},
+  journal      = {CoRR},
+  volume       = {abs/1705.07874},
+  year         = {2017},
+  url          = {http://arxiv.org/abs/1705.07874},
+  eprinttype    = {arXiv},
+  eprint       = {1705.07874},
+  timestamp    = {Fri, 26 Nov 2021 16:33:36 +0100},
+  biburl       = {https://dblp.org/rec/journals/corr/LundbergL17.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org},
+  doi          = {https://doi.org/10.48550/arXiv.1705.07874},
+}
+
+@article{shapley1953value,
+  title={A value for n-person games},
+  author={Shapley, Lloyd S and others},
+  year={1953},
+  publisher={Princeton University Press Princeton},
+  doi = {https://doi.org/10.1515/9781400881970-018},
+}
+
+@book{molnar2025,
+  title={Interpretable Machine Learning},
+  subtitle={A Guide for Making Black Box Models Explainable},
+  author={Christoph Molnar},
+  year={2025},
+  edition={3},
+  isbn={978-3-911578-03-5},
+  url={https://christophm.github.io/interpretable-ml-book}
+}
+
+@article{DBLP:journals/corr/abs-1802-03888,
+  author       = {Scott M. Lundberg and
+                  Gabriel G. Erion and
+                  Su{-}In Lee},
+  title        = {Consistent Individualized Feature Attribution for Tree Ensembles},
+  journal      = {CoRR},
+  volume       = {abs/1802.03888},
+  year         = {2018},
+  doi          = {https://doi.org/10.48550/arXiv.1802.03888},
+  url          = {http://arxiv.org/abs/1802.03888},
+  eprinttype    = {arXiv},
+  eprint       = {1802.03888},
+  timestamp    = {Mon, 13 Aug 2018 16:46:31 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1802-03888.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+
+@article{lundberg2020local2global,
+  title={From local explanations to global understanding with explainable AI for trees},
+  author={Lundberg, Scott M. and Erion, Gabriel and Chen, Hugh and DeGrave, Alex and Prutkin, Jordan M. and Nair, Bala and Katz, Ronit and Himmelfarb, Jonathan and Bansal, Nisha and Lee, Su-In},
+  journal={Nature Machine Intelligence},
+  volume={2},
+  number={1},
+  pages={2522-5839},
+  year={2020},
+  doi = {https://doi.org/10.1038/s42256-019-0138-9},
+  publisher={Nature Publishing Group}
+}
+
+
+@article{DBLP:journals/corr/ShrikumarGK17,
+  author       = {Avanti Shrikumar and
+                  Peyton Greenside and
+                  Anshul Kundaje},
+  title        = {Learning Important Features Through Propagating Activation Differences},
+  journal      = {CoRR},
+  volume       = {abs/1704.02685},
+  year         = {2017},
+  doi          = {https://doi.org/10.48550/arXiv.1704.02685},
+  url          = {http://arxiv.org/abs/1704.02685},
+  eprinttype    = {arXiv},
+  eprint       = {1704.02685},
+  timestamp    = {Thu, 14 Oct 2021 09:14:21 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/ShrikumarGK17.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+
+
+@article{friedman2001greedy,
+  title={Greedy function approximation: a gradient boosting machine},
+  author={Friedman, Jerome H},
+  journal={Annals of statistics},
+  pages={1189--1232},
+  year={2001},
+  doi = {https://doi.org/10.1214/aos/1013203451},
+  publisher={JSTOR}
+}
+
+@inproceedings{NIPS2017_6449f44a,
+ author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
+ pages = {},
+ publisher = {Curran Associates, Inc.},
+ title = {LightGBM: A Highly Efficient Gradient Boosting Decision Tree},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf},
+ volume = {30},
+ year = {2017}
+}
+
+@article{Moro2014ADA,
+  author    = {S{\'e}rgio Moro and P. Cortez and Paulo Rita},
+  title     = {A Data-Driven Approach to Predict the Success of Bank Telemarketing},
+  journal   = {Decision Support Systems},
+  volume    = {62},
+  pages     = {22--31},
+  year      = {2014},
+  doi       = {https://doi.org/10.1016/j.dss.2014.03.001},
+}
+
+@article{lecun1998gradient,
+  author  = {LeCun, Yann and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick},
+  title   = {Gradient-based Learning Applied to Document Recognition},
+  journal = {Proceedings of the IEEE},
+  volume  = {86},
+  number  = {11},
+  pages   = {2278--2324},
+  year    = {1998},
+  doi     = {https://doi.org/10.1109/5.726791}
+}
+
+@article{Krizhevsky_2017,
+  title={ImageNet classification with deep convolutional neural networks},
+  volume={60},
+  ISSN={1557-7317},
+  url={http://dx.doi.org/10.1145/3065386},
+  doi={https://doi.org/10.1145/3065386},
+  number={6},
+  journal={Communications of the ACM},
+  publisher={Association for Computing Machinery (ACM)},
+  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
+  year={2017},
+  month=may,
+  pages={84–90}
+}
+
+@misc{mitchell2022gputreeshapmassivelyparallelexact,
+  title        = {GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles},
+  author       = {Mitchell, Rory and Frank, Eibe and Holmes, Geoffrey},
+  year         = {2022},
+  doi          = {https://doi.org/10.48550/arXiv.2010.13972},
+  eprint       = {2010.13972},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.LG},
+  url          = {https://arxiv.org/abs/2010.13972},
+}
+
+@misc{UCI_ML_Repository,
+  author = {Kelly, Markelle and Longjohn, Rachel and Nottingham, Kolby},
+  title = {The UCI Machine Learning Repository},
+  url = {https://archive.ics.uci.edu},
+  institution = {University of California, Irvine}
+}
diff --git a/papers/avik_basu/myst.yml b/papers/avik_basu/myst.yml
@@ -0,0 +1,51 @@
+version: 1
+extends: ../papers.yml
+project:
+  # Update this to match `scipy-2025-<folder>` the folder should be `<firstname_surname>`
+  id: scipy-2025-avik_basu
+  # Ensure your title is the same as in your `main.md`
+  title: Explaining ML predictions with SHAP
+  subtitle: Intuition, use cases, and best practices
+  description: | 
+    This article explores how SHAP (SHapley Additive exPlanations) can be used to interpret machine learning model 
+    predictions by providing consistent and theoretically grounded feature attributions.
+  # Authors should have affiliations, emails and ORCIDs if available
+  authors:
+    - name: Avik Basu
+      email: avik_basu@intuit.com
+      orcid: 0009-0002-4489-378X
+      affiliations:
+        - Intuit, Inc.
+        - PyOpenSci
+      roles:
+        - Conceptualization
+        - Software
+        - Visualization
+        - Writing – original draft
+  keywords:
+    - explainable AI
+    - Shapley values
+  # Add the abbreviations that you use in your paper here
+  abbreviations:
+    MyST: Markedly Structured Text
+    SHAP: SHapley Additive exPlanations
+    LLM: Large Language Model
+    CNN: Convolutional Neural Network
+    GBDT: Gradient Boosted Decision Trees
+  # It is possible to explicitly ignore the `doi-exists` check for certain citation keys
+  error_rules:
+    - rule: doi-exists
+      severity: ignore
+      keys:
+        - jupyter
+        - sklearn1
+        - sklearn2
+        - NIPS2017_6449f44a
+        - molnar2025
+        - UCI_ML_Repository
+  exports:
+    - id: pdf
+      format: typst
+      template: https://github.com/curvenote-templates/scipy.git
+      article: main.md
+      output: full_text.pdf