Bibliography
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M.,
Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., and
Herrera, F. (2023), “Explainable artificial intelligence (XAI):
What we know and what is left to attain trustworthy artificial
intelligence,” Information Fusion, 99, 101805.
https://doi.org/https://doi.org/10.1016/j.inffus.2023.101805.
Apley, D. W., and Zhu, J. (2020), “Visualizing the effects of
predictor variables in black box supervised learning models,”
Journal of the Royal Statistical Society Series B: Statistical
Methodology, Oxford University Press, 82, 1059–1086.
Baniecki, H., and Biecek, P. (2019), “modelStudio: Interactive Studio with Explanations for ML
Predictive Models,” Journal of Open Source
Software, 4, 1798.
Baniecki, H., Parzych, D., and Biecek, P. (2023), “The grammar of
interactive explanatory model analysis,” Data Mining and
Knowledge Discovery, 1–37.
Bishop, C. M. (2006), Pattern recognition and machine learning,
Information science and statistics, New York: Springer.
Breiman, L. (2001), “Statistical modeling: The two cultures (with
comments and a rejoinder by the author),” Statistical
Science, Institute of Mathematical Statistics, 16, 199–231. https://doi.org/10.1214/ss/1009213726.
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y.,
Allen, J., McPherson, J., Dipert, A., and Borges, B. (2024), Shiny: Web application
framework for r.
Clarke, E., Sherrill-Mix, S., and Dawson, C. (2023), Ggbeeswarm:
Categorical scatter (violin point) plots.
Cook, D., Buja, A., Cabrera, J., and Hurley, C. (1995), “Grand
tour and projection pursuit,” Journal of Computational and
Graphical Statistics, Taylor & Francis, 4, 155–172.
Dandl, S., Molnar, C., Binder, M., and Bischl, B. (2020),
“Multi-Objective Counterfactual
Explanations,” in Parallel Problem
Solving from Nature – PPSN
XVI, eds. T. Bäck, M. Preuss, A. Deutz, H. Wang, C.
Doerr, M. Emmerich, and H. Trautmann, Cham: Springer International
Publishing, pp. 448–469. https://doi.org/10.1007/978-3-030-58112-1_31.
Friedman, J. H. (2001), “Greedy function approximation: A gradient
boosting machine,” Annals of statistics, JSTOR,
1189–1232.
Goodfellow, I., Bengio, Y., and Courville, A. (2016), Deep
learning, MIT Press.
Greenwell, B. M., and others (2017),
“Pdp: An r package for constructing partial dependence
plots.” R J., 9, 421.
Hastie, T., Tibshirani, R., and Friedman, J. H. (2009), The elements
of statistical learning: Data mining, inference, and prediction,
Springer series in statistics, New York, NY: Springer.
Hellweg, T. (2023), Anchors: AnchorsOnR: High-precision
model-agnostic explanations in r.
Ivala, E., Gachago, D., Condy, J., and Chigona, A. (2013),
“Enhancing Student Engagement with
Their Studies: A
Digital Storytelling
Approach,” Creative Education, 04, 82–89.
https://doi.org/10.4236/ce.2013.410A012.
James, G., Witten, D., Hastie, T., Tibshirani, R.,
and others (2013), An introduction to statistical
learning, Springer.
Kingma, D. P., and Ba, J. (2017), “Adam: A
Method for Stochastic
Optimization,” arXiv. https://doi.org/10.48550/arXiv.1412.6980.
Lipovetsky, S., and Conklin, M. (2001), “Analysis of regression in
game theory approach,” Applied stochastic models in business
and industry, Wiley Online Library, 17, 319–330.
Lundberg, S. M., and Lee, S.-I. (2017), “A unified approach to
interpreting model predictions,” Advances in neural
information processing systems, 30.
Mayer, M., and Watson, D. (2024), Kernelshap: Kernel
SHAP.
Molnar, C. (2022), Interpretable machine learning: A guide for
making black box models explainable, Munich, Germany: Christoph
Molnar.
Molnar, C., Casalicchio, G., and Bischl, B. (2020), “Interpretable
Machine Learning – A
Brief History,
State-of-the-Art and
Challenges,” pp. 417–431.
Olah, C., Mordvintsev, A., and Schubert, L. (2017), “Feature
visualization,” Distill, 2, e7.
Ooms, J., Kornel Lesiński, and Authors of the dependency Rust crates
(2025), Gifski:
Highest quality GIF encoder.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,
Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf,
A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,
Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019), “PyTorch:
An imperative style, high-performance deep learning library,”
in Advances in neural information processing systems 32, Curran
Associates, Inc., pp. 8024–8035.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016),
“"Why Should I
Trust You?": Explaining the
Predictions of Any
Classifier.” https://doi.org/10.48550/ARXIV.1602.04938.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2018), “Anchors:
High-Precision
Model-Agnostic
Explanations,” Proceedings of the AAAI
Conference on Artificial Intelligence, 32. https://doi.org/10.1609/aaai.v32i1.11491.
Shlens, J. (2014), “A tutorial on principal component
analysis,” arXiv preprint arXiv:1404.1100.
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013), “Deep inside
convolutional networks: Visualising image classification models and
saliency maps,” arXiv preprint arXiv:1312.6034.
Stachura, F., Krzeminski, D., Igras, K., Forys, A., Przytuła, P.,
Chojna, J., Mierzwa-Sulima, O., Nowicki, J., and Makowski, T. (2024),
Shiny.semantic:
Semantic UI support for shiny.
Staff, A. G. (2018), “8
adorable Aussie desert-dwellers,” Australian
Geographic.
Ushey, K. (2022), renv: Project environments.
Van Rossum, G., and Drake, F. L. (2009), Python 3 reference
manual, Scotts Valley, CA: CreateSpace.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez,
A. N., Kaiser, Ł., and Polosukhin, I. (2017), “Attention is all
you need,” Advances in neural information processing
systems, 30.
Vaughan, D., and Dancho, M. (2022), Furrr: Apply mapping
functions in parallel using futures.
Vaughan, D., Hester, J., Kalinowski, T., Landau, W., Lawrence, M.,
Maechler, M., Tierney, L., and Wickham, H. (2023), S7: An object
oriented system meant to become a successor to S3 and S4.
Wachter, S., Mittelstadt, B., and Russell, C. (2018), “Counterfactual
Explanations without Opening the
Black Box: Automated
Decisions and the GDPR,” arXiv.
Wickham, H. (2010), “A layered grammar of graphics,”
Journal of computational and graphical statistics, Taylor &
Francis, 19, 3–28.
Wickham, H. (2016), ggplot2:
Elegant graphics for data analysis, Springer-Verlag New York.
Wickham, H., Hester, J., Chang, W., Müller, K., and Cook, D. (2021),
Memoise:
’Memoisation’ of functions.
Yang, J. (2021), “Fast treeshap: Accelerating shap value
computation for trees,” arXiv preprint arXiv:2109.09847.
Żyła, K., Nowicki, J., Siemiński, L., Rogala, M., Vibal, R., Makowski,
T., and Basa, R. (2025), Rhino: A framework for
enterprise shiny applications.