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Applied Predictive Modeling
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In the citing paper
“[L1.1] No test set. Using the same dataset for training and testing the model is a textbook example of overfitting, which leads to overoptimistic performance estimates.54”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [54]
In the citing paper
“Following recommendations by Kuhn and Johnson,31 we used 5-fold cross-validation with 3 repetitions to select the optimal λ (shrinkage) and α (mixing) parameters for each elastic net model. This also allowed us to obtain a cross-validation estimate of model performance.”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [31]
In the citing paper
“Random forests can naturally be adapted to fit the unbalanced data framework by down-sampling the majority class and growing each tree on a more balanced data set (Chen et al., 2004; Kuhn and Johnson, 2013).”
From A random forest guided tour (2016) · cites this paper as Kuhn and Johnson, 2013
Citations
Cited by
Sorted by citations
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Prediction of Genetic Interactions Using Machine Learning and Network Properties
Madhukar, N. S., Elemento, O., & Pandey, G. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00172 · Cited by 39
“Finally, predictive models (Kuhn and Johnson, 2013) are learnt from this training set.”
MethodBrankovic, A., Cook, D., Rahman, J., Delaforce, A., Li, J., Magrabi, F., Cabitza, F., Coiera, E., & Bradford, D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01764-2 · Cited by 37
Conceptualizing vacation dedication
Ring, A., Yanamandram, V., Gretzel, U., & Dolnicar, S. (2020) · Current Issues in Tourism · doi:10.1080/13683500.2019.1678572 · Cited by 34
Lu, Y., Lavoie‐Gagne, O., Forlenza, E. M., Pareek, A., Kunze, K. N., Forsythe, B., Levy, B. A., & Krych, A. J. (2022) · Arthroscopy · doi:10.1016/j.arthro.2021.12.012 · Cited by 28
Açikgöz, F. Y., Kayakuş, M., Zăbavă, B.-Ș., & Kabas, O. (2024) · Sustainability · doi:10.3390/su16229681 · Cited by 25
Using machine learning to model trace behavioral data from a game‐based assessment
Auer, E. M., Mersy, G., Marin, S., Blaik, J., & Landers, R. N. (2022) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12363 · Cited by 24
Bogue-Jimenez, B., Huang, X., Powell, D., & Doblas, A. (2022) · Sensors · doi:10.3390/s22093534 · Cited by 24
Predictive Modeling of Bovine Respiratory Disease Outcomes in Feedlot Cattle: A Narrative Review
Wisnieski, L., Amrine, D. E., & Renter, D. G. (2021) · Livestock Science · doi:10.1016/j.livsci.2021.104666 · Cited by 23
Predicting domestic abuse (fairly) and police risk assessment
Turner, E., Brown, G., & Medina-Ariza, J. (2022) · Psychosocial Intervention · doi:10.5093/pi2022a11 · Cited by 22
MacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21
Chiu, Y. M., Courteau, J., Dufour, I., Vanasse, A., & Hudon, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-27568-6 · Cited by 20
Aguirre, U., & Urrechaga, E. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-0713 · Cited by 17
Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait
Fernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., Ferreira, C., Gama, J., Erlhagen, W., & Bicho, E. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621466 · Cited by 17
Sexual addiction in drug addicts: The impact of drug of choice and poly-addiction
Antonio, N., Diehl, A., Niel, M., Pillon, S., Ratto, L., Pinheiro, M. C., Silveira, D., Otani, T. Z., Otani, V., Cordeiro, Q., et al. (2017) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.63.05.414 · Cited by 16
Bae, J., Logan, P. E., Acri, D. J., Bharthur, A., Nho, K., Saykin, A. J., Risacher, S. L., Nudelman, K., Polsinelli, A. J., Pentchev, V., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13319 · Cited by 14
Dallora, A. L., Kvist, O., Berglund, J. S., Ruiz, S. D., Boldt, M., Flodmark, C.-E., & Anderberg, P. (2020) · JMIR Medical Informatics · doi:10.2196/18846 · Cited by 13
“We referred to the following book by Kuhn and Johnson [41] for the specific algorithms and implementations used in this study.”
MethodVoice Biomarkers of Recovery From Acute Respiratory Illness
Tracey, B., Patel, S., Zhang, Y., Chappie, K., Volfson, D., Parisi, F., Adans-Dester, C., Bertacchi, F., Bonato, P., & Wacnik, P. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3137050 · Cited by 10
Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters
Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D. (2020) · doi:10.31234/osf.io/2yber · Cited by 12
Marte, M. J., Carpenter, E., Scimeca, M., Russell-Meill, M., Peñaloza, C., Grasemann, U., Miikkulainen, R., & Kiran, S. (2025) · Stroke · doi:10.1161/strokeaha.124.047867 · Cited by 9
Jiwani, Z., Goldberg, S. B., Stroud, J., Young, J., Curtin, J., Dunne, J. D., Simonsson, O., Webb, C. A., Carhart-Harris, R., & Schlosser, M. (2025) · PLOS ONE · doi:10.1371/journal.pone.0310160 · Cited by 6
Spedicato, G., Dutang, C., & Petrini, L. (2018) · Variance · doi:10.66573/001c.116043 · Cited by 5
Mendoza, A., Tume, S., Puri, K., Acosta, S., & Cavallaro, J. R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3510217 · Cited by 4
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
Tampu, I. E., Eklund, A., & Haj-Hosseini, N. (2022) · Scientific Data · doi:10.1038/s41597-022-01618-6 · Cited by 1
Berk, R. A., & Braverman, A. (2025) · arXiv · doi:10.48550/arxiv.2503.16118
“More recent work on the topic includes Waldman (2000), Christoffersen and Diebold (2009), Kuhn and Johnson (2013), Lin et al., 2017), and Rednick and colleagues (2021).”
Metadata
- Authors
- Kuhn, M., & Johnson, K.
- Venue
- Springer eBooks
- Publisher
- Springer New York
- Published
- 2013
- OpenAlex
- W429766147
- Cited by
- 6,788
- References
- 0
- Citations by year
- 2026: 564 · 2025: 813 · 2024: 727 · 2023: 776 · 2022: 810 · 2021: 815 · 2020: 715 · 2019: 492 · 2018: 382 · 2017: 271 · 2016: 198 · 2015: 156 · 2014: 61 · 2013: 5
- FWCI
- 32.11
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- predictive modeling, data preprocessing, data partitioning, model calibration, model selection, model evaluation, classification, regression analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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