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Mistakes in machine learning practice are commonplace, and can result in a loss of confidence in the findings and products of machine learning.
Ablation studies in artificial neural networks
R. Meyes, M. Lu, C. W. de Puiseau, and T. Meisen · 1901
Earlier work this paper cites.
On comparing classifiers: Pitfalls to avoid and a recommended approach
S. L. Salzberg · 1997
Earlier work this paper cites.
G. Vandewiele, I. Dehaene, G. Kovács, L. Sterckx, O. Janssens, F. Ongenae, F. De Backere, F. De Turck, K. Roelens, J. Decruyenaere, S. Van Hoecke, and T. Demeester · 2001
Earlier work this paper cites.
A hierarchy of limitations in machine learning
M. M. Malik · 2002
Earlier work this paper cites.
The supervised learning no-free-lunch theorems
D. H. Wolpert · 2002
Earlier work this paper cites.
A survey of convolutional neural networks: analysis, applications, and prospects
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou · 2004
Earlier work this paper cites.
A critical analysis of metrics used for measuring progress in artificial intelligence
K. Blagec, G. Dorffner, M. Moradi, and M. Samwald · 2008
Earlier work this paper cites.
A survey of cross-validation procedures for model selection
S. Arlot, A. Celisse, et al · 2010
Earlier work this paper cites.
On over-fitting in model selection and subsequent selection bias in performance evaluation
G. C. Cawley and N. L. Talbot · 2010
Earlier work this paper cites.
Deep learning in neural networks: An overview
J. Schmidhuber · 2014
Earlier work this paper cites.
Hidden technical debt in machine learning systems
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J.-F. Crespo, and D. Dennison · 2015
Earlier work this paper cites.
Best (but oft-forgotten) practices: the multiple problems of multiplicity—whether and how to correct for many statistical tests
D. L. Streiner · 2015
Earlier work this paper cites.
Scikit-learn: Machine learning without learning the machinery
G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Mueller · 2015
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Learning from class-imbalanced data: Review of methods and applications
G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing · 2016
Earlier work this paper cites.
Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis
A. Benavoli, G. Corani, J. Demšar, and M. Zaffalon · 2017
Earlier work this paper cites.
Feature selection in machine learning: A new perspective
J. Cai, J. Luo, S. Wang, and S. Yang · 2017
Earlier work this paper cites.
Exploratory data analysis
V. Cox · 2017
Earlier work this paper cites.
The p-value requires context, not a threshold
R. A. Betensky · 2018
Earlier work this paper cites.
On reporting and interpreting statistical significance and p values in medical research
H. Aguinis, M. Vassar, and C. Wayant · 2019
Earlier work this paper cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
Earlier work this paper cites.
Mlj: A julia package for composable machine learning
A. D. Blaom, F. Kiraly, T. Lienart, Y. Simillides, D. Arenas, and S. J. Vollmer · 2020
Earlier work this paper cites.
Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review
J. Carrasco, S. García, M. Rueda, S. Das, and F. Herrera · 2020
Earlier work this paper cites.
Evaluating time series forecasting models: An empirical study on performance estimation methods
V. Cerqueira, L. Torgo, and I. Mozetič · 2020
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Developments in mlflow: A system to accelerate the machine learning lifecycle
A. Chen, A. Chow, A. Davidson, A. DCunha, A. Ghodsi, S. A. Hong, A. Konwinski, C. Mewald, S. Murching, T. Nykodym, et al · 2020
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A survey on ensemble learning
X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma · 2020
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I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data
M. Hosseini, M. Powell, J. Collins, C. Callahan-Flintoft, W. Jones, H. Bowman, and B. Wyble · 2020
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Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles , 2020
M. Kuhn and H. Wickham · 2020
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Information leakage in backtesting
W. Wang and J. Ruf · 2022
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Navigating the pitfalls of applying machine learning in genomics
S. Whalen, J. Schreiber, W. S. Noble, and K. S. Pollard · 2022
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Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence
S. Ali, T. Abuhmed, S. El-Sappagh, K. Muhammad, J. M. Alonso-Moral, R. Confalonieri, R. Guidotti, J. Del Ser, N. Díaz-Rodríguez, and F. Herrera · 2023
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Eight years of automl: categorisation, review and trends
R. Barbudo, S. Ventura, and J. R. Romero · 2023
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Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A.-L. Boulesteix, et al · 2023
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Explainable ai (xai): Core ideas, techniques, and solutions
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C. Molnar, G. König, J. Herbinger, T. Freiesleben, S. Dandl, C. A. Scholbeck, G. Casalicchio, M. Grosse-Wentrup, and B. Bischl · 2020
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Model evaluation, model selection, and algorithm selection in machine learning
S. Raschka · 2020
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Recommendations for reporting machine learning analyses in clinical research
L. M. Stevens, B. J. Mortazavi, R. C. Deo, L. Curtis, and D. P. Kao · 2020
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A survey on missing data in machine learning
T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona · 2021
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Pre-trained models: Past, present and future
X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang, et al · 2021
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Are we learning yet? a meta review of evaluation failures across machine learning
T. Liao, R. Taori, I. D. Raji, and L. Schmidt · 2021
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Data and its (dis)contents: A survey of dataset development and use in machine learning research
A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna · 2021
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R. Dwivedi, D. Dave, H. Naik, S. Singhal, R. Omer, P. Patel, B. Qian, Z. Wen, T. Shah, G. Morgan, et al · 2023
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Forecast evaluation for data scientists: common pitfalls and best practices
H. Hewamalage, K. Ackermann, and C. Bergmeir · 2023
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Data augmentation techniques in time series domain: a survey and taxonomy
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Leakage and the reproducibility crisis in machine-learning-based science
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Machine learning operations (MLOps): Overview, definition, and architecture
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Privacy in large language models: Attacks, defenses and future directions
H. Li, Y. Chen, J. Luo, Y. Kang, X. Zhang, Q. Hu, C. Chan, and Y. Song · 2023
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Big little lies: A compendium and simulation of p-hacking strategies
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Are transformers effective for time series forecasting?
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Dive into deep learning
A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola · 2023
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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
C. Zhou, Q. Li, C. Li, J. Yu, Y. Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He, et al · 2023
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Machine learning in environmental research: common pitfalls and best practices
J.-J. Zhu, M. Yang, and Z. J. Ren · 2023
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Fairness in machine learning: A survey
S. Caton and C. Haas · 2024
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Data cleaning and machine learning: a systematic literature review
P.-O. Côté, A. Nikanjam, N. Ahmed, D. Humeniuk, and F. Khomh · 2024
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REFORMS: Consensus-based recommendations for machine-learning-based science
S. Kapoor, E. M. Cantrell, K. Peng, T. H. Pham, C. A. Bail, O. E. Gundersen, J. M. Hofman, J. Hullman, M. A. Lones, M. M. Malik, P. Nanayakkara, R. A. Poldrack, I. D. Raji, M. Roberts, M. J. Salganik, M. Serra-Garcia, B. M. Stewart, G. Vandewiele, and A. Narayanan · 2024
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Avoiding machine learning pitfalls
M. A. Lones · 2024
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A comprehensive survey on data augmentation
Z. Wang, P. Wang, K. Liu, P. Wang, Y. Fu, C.-T. Lu, C. C. Aggarwal, J. Pei, and Y. Zhou · 2024
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