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@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {http://yihui.org/knitr/},
}
@Manual{rlang,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2018},
url = {https://www.R-project.org/},
}
@Book{EMA,
author = {Przemyslaw Biecek and Tomasz Burzykowski},
title = {{Explanatory Model Analysis}},
publisher = {Chapman and Hall/CRC, New York},
year = {2021},
isbn = {9780367135591},
url = {https://pbiecek.github.io/ema/},
}
@article{wb_2020,
author = {WUoT},
title = {ML Case Studies: Reproducibility of scientific papers},
year = {2020},
url = {https://mini-pw.github.io/2020L-WB-Book/reproducibility.html}
}
@InProceedings{4-reproducedpapers,
author="Yildiz, Burak
and Hung, Hayley
and Krijthe, Jesse H.
and Liem, Cynthia C. S.
and Loog, Marco
and Migut, Gosia
and Oliehoek, Frans A.
and Panichella, Annibale
and Pawe{\l}czak, Przemys{\l}aw
and Picek, Stjepan
and de Weerdt, Mathijs
and van Gemert, Jan",
editor="Kerautret, Bertrand
and Colom, Miguel
and Kr{\"a}henb{\"u}hl, Adrien
and Lopresti, Daniel
and Monasse, Pascal
and Talbot, Hugues",
title="ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility",
booktitle="Reproducible Research in Pattern Recognition",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="3--11",
abstract="We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.",
isbn="978-3-030-76423-4"
}
@article{4-open_science,
title={Open Science in Software Engineering},
ISBN={9783030324896},
url={http://dx.doi.org/10.1007/978-3-030-32489-6_17},
DOI={10.1007/978-3-030-32489-6_17},
journal={Contemporary Empirical Methods in Software Engineering},
publisher={Springer International Publishing},
author={Mendez, Daniel and Graziotin, Daniel and Wagner, Stefan and Seibold, Heidi},
year={2020},
pages={477–501}
}
@misc{4-reproducibility,
title={On the Replicability and Reproducibility of Deep Learning in Software Engineering},
author={Chao Liu and Cuiyun Gao and Xin Xia and David Lo and John Grundy and Xiaohu Yang},
year={2020},
eprint={2006.14244},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
@article{scikitlearn,
title={Scikit-learn: Machine Learning in {P}ython},
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.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
@article{dalex,
title={{dalex: Responsible Machine Learning with Interactive
Explainability and Fairness in Python}},
author={Hubert Baniecki and Wojciech Kretowicz and Piotr Piatyszek
and Jakub Wisniewski and Przemyslaw Biecek},
year={2020},
journal={arXiv:2012.14406},
url={https://arxiv.org/abs/2012.14406}
}
@article{xai1-breakdown,
author = {Mateusz Staniak and Przemysław Biecek},
title = {Explanations of model predictions with live and breakDown packages},
year = {2018}}
@incollection{xai1-shapleyvalues,
title = {A unified approach to interpreting model predictions},
author = {Lundberg, Scott M and Lee, Su-In},
year = 2017,
booktitle = {Advances in Neural Information Processing Systems 30},
publisher = {Curran Associates},
address = {Montreal},
pages = {4765--4774},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}
}
@inproceedings{xai1-lime,
author = {Marco Tulio Ribeiro and
Sameer Singh and
Carlos Guestrin},
title = {"{Why should I trust you}?": explaining the predictions of any classifier},
booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on
Knowledge Discovery and Data Mining, KDD San Francisco, CA},
pages = {1135--1144},
year = {2016},
publisher = {Association for Computing Machinery},
address = {New York, NY}
}
@article{xai1-ice,
title = {Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation},
author = {Alex Goldstein and Adam Kapelner and Justin Bleich and Emil Pitkin},
year = {2014}}
@article{xai1-vip,
author = {Aaron Fisher and Cynthia Rudin and Francesca Dominici},
title = {All models are Wrong, but many are Useful: Learning a variable's importance by studying an entire class of prediction models simultaneously},
journal = {Journal of Machine Learning Research},
year = {2019},
volume = {20},
number = {177},
pages = {1-81},
url = {http://jmlr.org/papers/v20/18-760.html}
}
@article{xai1-pdp,
title = {Greedy Function Approximation: A Gradient Boosting Machine},
author = {Jerome H. Friedman},
year = 2000,
journal = {Annals of Statistics},
volume = 29,
pages = {1189--1232}
}
@article{xai1-ale,
title = {{Visualizing the effects of predictor variables in black box supervised learning models}},
author={Daniel W. Apley and Jingyu Zhu},
journal={Journal of the Royal Statistical Society Series B},
year=2020,
volume={82},
number={4},
pages={1059--1086},
month={September},
keywords={},
doi={10.1111/rssb.12377}
}
@manual{EUGDPR,
title = {{The EU General Data Protection Regulation (GDPR) is the most important change in data privacy regulation in 20 years}},
author = {{GDPR}},
year = 2018,
url = {https://eugdpr.org/}
}
@article{1-2-german_determinants,
title = {Fundamental determinants of real estate prices: A panel study of German regions},
address = {Essen},
author = {Ansgar Belke and Jonas Keil},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
doi = {10.4419/86788851},
isbn = {978-3-86788-851-6},
number = {731},
publisher = {RWI - Leibniz-Institut f\"{u}r Wirtschaftsforschung},
type = {Ruhr Economic Papers},
url = {http://hdl.handle.net/10419/173207},
year = {2017}
}
@article{1-2-forecasting,
title = {Chapter 9 - Forecasting Real Estate Prices},
editor = {Graham Elliott and Allan Timmermann},
series = {Handbook of Economic Forecasting},
publisher = {Elsevier},
volume = {2},
pages = {509-580},
year = {2013},
booktitle = {Handbook of Economic Forecasting},
issn = {1574-0706},
doi = {https://doi.org/10.1016/B978-0-444-53683-9.00009-8},
url = {https://www.sciencedirect.com/science/article/pii/B9780444536839000098},
author = {Eric Ghysels and Alberto Plazzi and Rossen Valkanov and Walter Torous},
keywords = {Real estate, Predictability, Market efficiency, REIT}
}
@article{1-2-seba1,
title = {Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data},
journal = {Expert Systems with Applications},
volume = {42},
number = {6},
pages = {2928-2934},
year = {2015},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2014.11.040},
url = {https://www.sciencedirect.com/science/article/pii/S0957417414007325},
author = {Byeonghwa Park and Jae Kwon Bae},
keywords = {Housing price index, Housing price prediction model, Machine learning algorithms, C4.5, RIPPER, Naïve Bayesian, AdaBoost}}
}
@article{1-2-seba2,
author = {Ming, Yue and Zhang, Jie and Qi, Jiaming and Liao, Tian and Wang, Maolin and Zhang, Lingli},
title = {Prediction and Analysis of Chengdu Housing Rent Based on XGBoost Algorithm},
year = {2020},
isbn = {9781450387859},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3422713.3422720},
doi = {10.1145/3422713.3422720},
booktitle = {Proceedings of the 2020 3rd International Conference on Big Data Technologies},
pages = {1–5},
numpages = {5},
keywords = {LightGBM, Visual analysis, Housing rent forecast, XGBoost},
location = {Qingdao, China},
series = {ICBDT 2020}
}
@article{1-2-seba3,
author={Limsombunchao, V.},
title={House price prediction: Hedonic price model vs.artificial neural network},
type={Conference Contribution - Published (Conference Paper)},
year={2004},
series={Department of Financial and Business Systems [509]},
publisher={New Zealand Agricultural and Resource Economics Society},
location={Blenheim, New Zealand}
}
@article{1-2-maciej1,
author={Z. {Peng} and Q. {Huang} and Y. {Han}},
booktitle={2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT)},
title={Model Research on Forecast of Second-Hand House Price in Chengdu Based on XGboost Algorithm},
year={2019},
volume={},
number={},
pages={168-172},
doi={10.1109/ICAIT.2019.8935894}}
@article{1-2-maciej2,
author={J. {Manasa} and R. {Gupta} and N. S. {Narahari}},
booktitle={2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)},
title={Machine Learning based Predicting House Prices using Regression Techniques},
year={2020},
volume={},
number={},
pages={624-630},
doi={10.1109/ICIMIA48430.2020.9074952}}
@article{1-2-maciej3,
author={Y. {Zhao} and G. {Chetty} and D. {Tran}},
booktitle={2019 IEEE Symposium Series on Computational Intelligence (SSCI)},
title={Deep Learning with XGBoost for Real Estate Appraisal},
year={2019},
volume={},
number={},
pages={1396-1401},
doi={10.1109/SSCI44817.2019.9002790}}
@article{1-2-maciej4,
author={A. {Varma} and A. {Sarma} and S. {Doshi} and R. {Nair}},
booktitle={2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)},
title={House Price Prediction Using Machine Learning and Neural Networks},
year={2018},
volume={},
number={},
pages={1936-1939},
doi={10.1109/ICICCT.2018.8473231}}
@article{1-5-hotels-automated-ml-system,
title = {An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations},
author = {Nuno Antonio and Ana {de Almeida} and Luis Nunes},
journal = {Data Science Journal},
volume = {18},
publisher = {Ubiquity Press},
number = {1},
year = {2019},
pages = {1--20},
doi = {10.5334/dsj-2019-032}
}
@article{1-5-hotels-predicting-to-decrease-uncertainty-increase-revenue,
title = {Predicting hotel booking cancellations to decrease uncertainty and increase revenue},
author = {Nuno Antonio and Ana {de Almeida} and Luis Nunes},
journal = {Tourism & Management Studies},
volume = {13},
publisher = {University of the Algarve - ESGHT - CIEO},
number = {2},
year = {2017},
pages = {25--39},
doi = {10.18089/tms.2017.13203}
}
@article{1-5-hotels-cancellation-policies-shift,
title = {A paradigm shift in revenue management? The new landscape of hotel cancellation policies},
author = {Arash Riasi and Zvi Schwartz and Chih-Chien Chen},
journal = {Journal of Revenue and Pricing Management},
volume = {18},
publisher = {Palgrave Macmillan},
number = {6},
year = {2019},
pages = {434--440},
doi = {10.1057/s41272-019-00189-3}
}
@article{1-5-hotels-modelling-cancellation-behaviour,
title = {Modelling the cancellation behaviour of hotel guests},
author = {Martin Falk and Markku Vieru},
journal = {International Journal of Contemporary Hospitality Management},
volume = {30},
publisher = {Emerald Publishing Limited},
number = {10},
year = {2018},
pages = {3100--3116},
doi = {10.1108/ijchm-08-2017-0509}
}
@inproceedings{1-5-hotels-prediction-using-crisp-dm,
title = {Prediction of Hotel Booking Cancellation using CRISP-DM},
author = {Z. A. {Andriawan} and S. R. {Purnama} and A. S. {Darmawan} and {Ricko} and A. {Wibowo} and A. {Sugiharto} and F. {Wijayanto}},
booktitle = {2020 4th International Conference on Informatics and Computational Sciences (ICICoS)},
year = {2020},
pages = {1--6},
doi = {10.1109/ICICoS51170.2020.9299011}
}
@article{1-5-hotels-efficient-forecasting,
title = {Using machine learning and big data for efficient forecasting of hotel booking cancellations},
author = {Agustín J Sánchez-Medina and Eleazar C-Sánchez},
journal = {International Journal of Hospitality Management},
volume = {89},
publisher = {Elsevier Ltd},
year = {2020},
pages = {102546},
doi = {10.1016/j.ijhm.2020.102546}
}
@article{1-5-cancellations-policies-study,
title = {Hotel Cancelation Policies, Distributive and Procedural Fairness, and Consumer Patronage: A Study of the Lodging Industry},
author = {Scott J. Smith and H.G. Parsa and Milos Bujisic and Jean-Pierre {van der Rest}},
journal = {Journal of Travel & Tourism Marketing},
volume = {32},
publisher= {Taylor & Francis (Routledge)},
year = {2015},
pages = {886--906},
doi = {10.1080/10548408.2015.1063864}
}
@article{1-5-dataset,
title = {Hotel booking demand datasets},
journal = {Data in Brief},
volume = {22},
pages = {41-49},
year = {2019},
issn = {2352-3409},
doi = {10.1016/j.dib.2018.11.126},
url = {https://www.sciencedirect.com/science/article/pii/S2352340918315191},
author = {Nuno Antonio and Ana {de Almeida} and Luis Nunes}
}
@article{1-5-triplot,
author = {Katarzyna Pekala and
Katarzyna Woznica and
Przemyslaw Biecek},
title = {Triplot: model agnostic measures and visualisations for variable importance
in predictive models that take into account the hierarchical correlation
structure},
journal = {CoRR},
volume = {abs/2104.03403},
year = {2021},
url = {https://arxiv.org/abs/2104.03403},
archivePrefix = {arXiv},
eprint = {2104.03403},
timestamp = {Tue, 13 Apr 2021 16:46:17 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-03403.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{1-5-right-to-explanation,
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}
@techreport{5-2-fairlearn,
author = {Bird, Sarah and Dud{\'i}k, Miro and Edgar, Richard and Horn, Brandon and Lutz, Roman
and Milan, Vanessa and Sameki, Mehrnoosh and Wallach, Hanna and Walker, Kathleen},
title = {{Fairlearn: A toolkit for assessing and improving fairness in AI}},
institution = {Microsoft},
year = {2020},
url = {https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/},
number = {MSR-TR-2020-32}
}
@article{5-2-fairmodels,
title={{fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation}},
author={Jakub Wiśniewski and Przemysław Biecek},
year={2021},
journal={arXiv:2104.00507},
url={https://arxiv.org/abs/2104.00507}
}
@article{5-2-variableimportance,