The DOI matches its Crossref record.
Applied Predictive Modeling
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Items 801–900 of 1,024
Schwarzer, A., Talbot, S. R., Selich, A., Morgan, M. A., Schott, J. W., Dittrich‐Breiholz, O., Bastone, A. L., Weigel, B., Ha, T. C., Dziadek, V., et al. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2021.06.017 · Cited by 50
dos Santos, E. P., da Silva, D. D., do Amaral, C. H., Filho, E. I. F., & Dias, R. L. S. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106753 · Cited by 54
Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
Solanke, A. A., & Biasiotti, M. A. (2022) · Künstliche Intell. · doi:10.1007/s13218-022-00763-9 · Cited by 72
A Hybrid ConvLSTM-Based Anomaly Detection Approach for Combating Energy Theft
Gao, H., Kuenzel, S., & Zhang, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3201569 · Cited by 56
Assessment of water consumption in households using statistical analysis and regression trees
Grespan, A., Garcia, J., Brikalski, M. P., Henning, E., & Kalbusch, A. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104186 · Cited by 53
Suleymanov, A., Туктарова, И. Ф., Белан, Л. Н., et al. (2023) · Modeling Earth Systems and Environment · doi:10.1007/s40808-023-01723-4 · Cited by 51
“Cubist algorithm consists in growing a tree on which the final leaves contain linear regression models (Kuhn and Johnson 2013).”
MethodDrinkSense: Characterizing Youth Drinking Behavior Using Smartphones
Santani, D., Tri, T. M., Labhart, F., et al. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2797901 · Cited by 51
Qi, C., Fourie, A. B., Ma, G., & Tang, X. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.07.035 · Cited by 51
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis
Foulkes, A., Watson, D., Carr, D. F., Kenny, J. G., Slidel, T. W. F., Parslew, R., Pirmohamed, M., Anders, S., Reynolds, N. J., Griffiths, C. E. M., et al. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.04.041 · Cited by 54
“To do so, we created a pipeline using tools from the caret package for classification and regression training (Kuhn and Johnson, 2013).”
MethodHierarchical density-based cluster analysis framework for atom probe tomography data
Ghamarian, I., & Marquis, E. A. (2019) · Ultramicroscopy · doi:10.1016/j.ultramic.2019.01.011 · Cited by 50
der Donck, S. V., Dzhelyova, M. P., Vettori, S., Thielen, H., Steyaert, J. G., Rossion, B., & Boets, B. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04172-0 · Cited by 59
Identification of newborns at risk for autism using electronic medical records and machine learning
Rahman, R., Kodesh, A., Levine, S. Z., Sandin, S., Reichenberg, A., & Schlessinger, A. (2020) · European Psychiatry · doi:10.1192/j.eurpsy.2020.17 · Cited by 54
“Second, ML methods combine nonlinear interactions between features, which may not be captured by traditional modeling approaches (e.g., logistic regression model) [29].”
MethodImproving the delivery of palliative care through predictive modeling and healthcare informatics
Murphree, D. H., Wilson, P. M., Asai, S. W., Quest, D. J., Lin, Y., Mukherjee, P., Chhugani, N., Strand, J. J., Demuth, G., Mead, D. W., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa211 · Cited by 51
Khan, N. M., Cao, K., Yuan, Q., et al. (2022) · Sustainability · doi:10.3390/su14169901 · Cited by 51
RetractedRobles-Guerrero, A., Saucedo-Anaya, T., González, E., et al. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.02.024 · Cited by 50
Econometrics and Machine Learning
Arthur Charpentier, A., Flachaire, E., & Ly, A. (2019) · Economie et Statistique / Economics and Statistics · doi:10.24187/ecostat.2018.505d.1970 · Cited by 928
Ecosystems are showing symptoms of resilience loss
Rocha, J. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac73a8 · Cited by 59
“It is robust to potential correlations and required less pre-processing for feature engineering but is less amenable to interpretation (34).”
BackgroundDeveloping the Swiss mid-infrared soil spectral library for local estimation and monitoring
Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R. G., Wächter, D., Lee, J., Rossel, R. A. V., & Six, J. (2021) · SOIL · doi:10.5194/soil-7-525-2021 · Cited by 52
“…selection bias due to data leakage is well documented for so-called wrapper methods of variable selection, like RFE (Ambroise and McLachlan, 2002; Kuhn and Johnson, 2013), and occurs if these two tasks are not sufficiently separated by using independent data sets for each of them; this becomes…”
BackgroundMethodInfluentialPinto-Bernal, M. J., Cifuentes, C. A., Perdómo, O., Rincón-Roncancio, M., & Múnera, M. (2021) · Sensors · doi:10.3390/s21196401 · Cited by 50
A Machine Learning Model for Food Source Attribution of Listeria monocytogenes
Tanui, C. K., Benefo, E. O., Karanth, S., & Pradhan, A. K. (2022) · Pathogens · doi:10.3390/pathogens11060691 · Cited by 52
Amado, P. S. M., Woodley, C. M., Cristiano, M. L. S., & O’Neill, P. M. (2022) · ACS Omega · doi:10.1021/acsomega.2c05307 · Cited by 52
ESG Factors and Firms' Credit Risk
Bonacorsi, L., Cerasi, V., Galfrascoli, P., & Manera, M. (2024) · Journal of Climate Finance · doi:10.1016/j.jclimf.2024.100032 · Cited by 65
Data Preprocessing and Feature Engineering for Data Mining: Techniques, Tools, and Best Practices
Koukaras, P., & Tjortjis, C. (2025) · AI · doi:10.3390/ai6100257 · Cited by 96
Murrell, D. S., Cortés‐Ciriano, I., van Westen, G. J. P., Stott, I. P., Bender, A., Malliavin, T. E., & Glen, R. C. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0086-2 · Cited by 50
“descriptor centering and scaling to unit variance (function PreProcess) among others [30].”
BackgroundGaiad, J. E., Hidalgo, M. J., Villafañe, R. N., et al. (2016) · Microchemical Journal · doi:10.1016/j.microc.2016.07.002 · Cited by 50
Moclán, A., Domínguez‐Rodrigo, M., & Yravedra, J. (2019) · Archaeological and Anthropological Sciences · doi:10.1007/s12520-019-00815-6 · Cited by 50
“This is done by creating a per-class mixture (Kuhn and Johnson 2013).”
BackgroundMethodAttitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine
Lee, D., Mulrow, J., Haboucha, C. J., et al. (2019) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198119857953 · Cited by 50
“Specifically, single-tree models tend to have high variance and result in increasing the overall mean squared error (MSE) because of the additive learning process (11, 38, 39).”
BackgroundDing, J., Yang, S., Shi, Q., Wei, Y., & Wang, F. (2020) · Remote Sensing · doi:10.3390/rs12162601 · Cited by 51
“In this study, RF was used to calculate the importance of the predictors because it is not highly sensitive to data distribution [68]; the optimal subset was identified using the minimum OOB error.”
MethodPredicting the compressive strength of fly ash concrete with the Particle Model
Kang, S., Lloyd, Z., Kim, T., et al. (2020) · Cement and Concrete Research · doi:10.1016/j.cemconres.2020.106218 · Cited by 50
An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities
Pirbazari, A. M., Sharma, E., Chakravorty, A., Elmenreich, W., & Rong, C. (2021) · IEEE Access · doi:10.1109/access.2021.3063066 · Cited by 52
Luu, N. T., Le, H.-M., Phan, Q.-H., & Pham, T.-T.-H. (2021) · Journal of Biomedical Optics · doi:10.1117/1.jbo.26.7.075001 · Cited by 51
Lessons and tips for designing a machine learning study using EHR data
Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
Face Recognition System using Facenet Algorithm for Employee Presence
Cahyono, F. B., Wirawan, W., & Rachmadi, R. F. (2020) · International Conference on Vocational Education and Training (ICOVET) · doi:10.1109/icovet50258.2020.9229888 · Cited by 49
“When choosing k with a large value, the set for sample data will be smaller [17].”
BackgroundHazarika, R. A., Maji, A. K., Sur, S. N., Paul, B. S., & Kandar, D. (2021) · IEEE Access · doi:10.1109/access.2021.3072559 · Cited by 53
GEIN: An interpretable benchmarking framework towards all building types based on machine learning
Jin, X., Xiao, F., Zhang, C., & Li, A. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111909 · Cited by 49
Modelling soil thickness using environmental attributes in karst watersheds
Zhang, Y., Xu, X., Li, Z., Yi, R., Xu, C., & Luo, W. (2022) · CATENA · doi:10.1016/j.catena.2022.106053 · Cited by 53
Wallert, J., Boberg, J., Kaldo, V., Mataix-Cols, D., Flygare, O., Crowley, J. J., Halvorsen, M., Ben Abdesslem, F., Boman, M., Andersson, E., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02133-3 · Cited by 48
Tetereva, A., Li, J., Deng, J. D., Stringaris, A. K., & Pat, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119588 · Cited by 53
“Common algorithms include those based on penalised regression Kuhn and Johnson 2013 ), tree-based regression (Breiman et al. 2017) nd kernel-based regression ( Cortes and Vapnik 1995 ).”
BackgroundMethodAdvanced Data Analytics Techniques for Social Impact
Global, N., & Kazanskaia, A. N. (2025) · doi:10.64357/advanced-data-analytics-social-impact-2025 · Cited by 49
Maguire, A., Vega-Carrascal, I., Bryant, J., White, L., Howe, O. L., Lyng, F. M., & Meade, A. D. (2014) · The Analyst · doi:10.1039/c4an01887g · Cited by 48
Лю, Б., Hu, J., Yan, F., et al. (2016) · Measurement · doi:10.1016/j.measurement.2016.06.015 · Cited by 49
Unrecorded alcohol use: a global modelling study based on nominal group assessments and survey data
Probst, C., Manthey, J., Merey, A., Rylett, M., & Rehm, J. T. (2018) · Addiction · doi:10.1111/add.14173 · Cited by 51
“The precision of the model was evaluated usingR(2), representing the proportion of the variance in the observed data that is explained by the prediction model [39].”
MethodVulnerability of Cerrado threatened mammals: an integrative landscape and climate modeling approach
Júnior, P. D. M., Villén‐Pérez, S., Mendes, P., Nóbrega, C. C., Côrtes, L. G., Castro, T., & de Souza, R. A. (2018) · Biodiversity and Conservation · doi:10.1007/s10531-018-1615-x · Cited by 53
Liu, Y., Shi, Z., Zhang, G., et al. (2018) · Remote Sensing · doi:10.3390/rs10111747 · Cited by 49
Erickson, M. L., Elliott, S. M., Christenson, C., et al. (2018) · Water Resources Research · doi:10.1029/2018wr023106 · Cited by 49
“We therefore performed cross validation (CV) using the caret package (v. 6.0–76, Kuhn, 2014) to balance model complexity with performance predicting to new data (Kuhn & Johnson, 2013; Wenger & Olden, 2012).”
BackgroundMethodInfluentialEmergence of digital biomarkers to predict and modify treatment efficacy: machine learning study
Guthrie, N. L., Carpenter, J., Edwards, K. L., Appelbaum, K. J., Dey, S., Eisenberg, D. M., Katz, D. L., & Berman, M. A. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030710 · Cited by 49
The history, current status, and possible future of precision mental health
DeRubeis, R. J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103506 · Cited by 46
A survey and benchmarking study of multitreatment uplift modeling
Olaya, D., Coussement, K., & Verbeke, W. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00670-y · Cited by 49
Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25009 · Cited by 56
“This value of k has been shown to yield test error rate estimates that suffer neither from excessively high bias nor from very high variance (Kuhn and Johnson, 2013).”
BackgroundMazumdar, M., Lin, J.-Y., Zhang, W., Li, L., Liu, M., Dharmarajan, K., Sanderson, M., Isola, L., & Hu, L.-Y. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05148-y · Cited by 40
“This value usually correlates well with the assessment of predictive performance we can get with either cross-validation or from a test set [27].”
BackgroundWinship, A. J., Thorson, J. T., Clarke, M. E., et al. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00303 · Cited by 49
“Crossvalidation is a common form of data subsetting that is often combined with a model selection process to optimize a model’s predictive ability (Kuhn and Johnson, 2013).”
BackgroundSabetizade, M., Gorji, M., Roudier, P., Zolfaghari, A. A., & Keshavarzi, A. (2020) · CATENA · doi:10.1016/j.catena.2020.104844 · Cited by 47
Childhood trauma and amygdala nuclei volumes in youth at risk for mental illness
Nogovitsyn, N., Addington, J., de Souza, R. M., Placsko, T. J., Stowkowy, J., Wang, J., Goldstein, B. I., Bray, S., Lebel, C. A., Taylor, V. H., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291720003177 · Cited by 54
“Prior to the mediation analysis, we extracted a single principal component as a regression factor score (Kuhn & Johnson, 2013) derived from right Ba, right AB and right Co (sum of squared loadings 91.5%, direct oblimin rotation).”
MethodInfluentialWei, Y., Shi, Z., Biswas, A., Yang, S., Ding, J., & Wang, F. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135387 · Cited by 47
A Machine learning framework to predict reverse flow and water level: A case study of Tonle Sap Lake
Morovati, K., Nakhaei, P., Tian, F., Tudaji, M., & Hou, S. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.127168 · Cited by 51
Fairness-aware configuration of machine learning libraries
Tizpaz-Niari, S., Kumar, A., Tan, G., & Trivedi, A. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510202 · Cited by 67
Pat, N., Wang, Y., Bartonicek, A., Candia, J., & Stringaris, A. K. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac235 · Cited by 52
Camprubí, À. C., González‐Moreno, P., & de Dios, V. R. (2022) · Remote Sensing · doi:10.3390/rs14133162 · Cited by 48
“Poor performance of an RF-based model towards the extremes is a well-known problem within RF models [35].”
BackgroundInfluentialDynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data
Croci, M., Impollonia, G., Meroni, M., & Amaducci, S. (2022) · Remote Sensing · doi:10.3390/rs15010100 · Cited by 51
N‐SDM : a high‐performance computing pipeline for Nested Species Distribution Modelling
Adde, A., Rey, P., Brun, P., et al. (2023) · Ecography · doi:10.1111/ecog.06540 · Cited by 48
“Parallel grid-search approaches have been proven useful for automatic hyperparameter tuning and should be present in any new SDM platform (Kuhn and Johnson 2013, Chicco 2017, Vignali et al. 2020).”
MethodMachine learning for construction crew productivity prediction using daily work reports
Sadatnya, A., Sadeghi, N., Sabzekar, S., Khanjani, M., Tak, A. N., & Taghaddos, H. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104891 · Cited by 56
Consensus forecasting of intertidal seagrass habitat in the Wadden Sea
Folmer, E. O., van Beusekom, J. E. E., Dolch, T., Gräwe, U., van Katwijk, M. M., Kolbe, K., & Philippart, C. J. M. (2016) · Journal of Applied Ecology · doi:10.1111/1365-2664.12681 · Cited by 48
Meyer, H., Lehnert, L. W., Yun, W., et al. (2016) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2016.10.001 · Cited by 48
Valdes–Donoso, P., VanderWaal, K. L., Jarvis, L. S., et al. (2017) · Frontiers in Veterinary Science · doi:10.3389/fvets.2017.00002 · Cited by 48
“The RF approach has high accuracy without overfitting, it is also fairly stable to the presence of outliers and noise, and it may handle the correlation between predictors (40, 41, 53).”
BackgroundMethodInfluentialSuchting, R., Hébert, E. T., Ma, P., Kendzor, D. E., & Businelle, M. S. (2017) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntx201 · Cited by 48
Intergenerational mobility during the Great Depression
Feigenbaum, J. (2015) · OpenBU (Boston University) · Cited by 47
Karimi, S. S., Saintilan, N., Wen, L., & Valavi, R. (2019) · Water Resources Research · doi:10.1029/2019wr024884 · Cited by 51
“For each tree at each iteration, balanced bootstrap was sampled within a stratified sampling, as such, a bootstrap was chosen from the minority class and then the same number of cases drawn with replacement from the majority class (Chen et al., 2004; Kuhn & Johnson, 2013).”
MethodOptical Water Type Guided Approach to Estimate Optical Water Quality Parameters
Uudeberg, K., Aavaste, A., Kõks, K.-L., et al. (2020) · Remote Sensing · doi:10.3390/rs12060931 · Cited by 48
A two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company
Fonseca, D. P., Wänke, P., & Corrêa, H. L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106329 · Cited by 50
de Oliveira, L. A. B., & Carneiro, C. D. C. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.108080 · Cited by 48
Chung, Y.-M., Hu, C.-S., Lo, Y., & Wu, H. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.637684 · Cited by 54
Regional ensemble modeling reduces uncertainty for digital soil mapping
Brungard, C., Nauman, T., Duniway, M. C., Veblen, K. E., Nehring, K. C., White, D. A., Salley, S. W., & Anchang, J. Y. (2021) · Geoderma · doi:10.1016/j.geoderma.2021.114998 · Cited by 48
“The one-standard-error-rule (Hastie et al., 2001; Kuhn and Johnson, 2013), that is the tuning parameters that resulted in model accuracy within one standard error of the model with the absolute highest accuracy, was used to select the final model.”
MethodNasir, V., Kooshkbaghi, M., Cool, J., & Sassani, F. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06473-6 · Cited by 48
Priya, R. L., Jinny, S. V., & Mate, Y. (2020) · Health and Technology · doi:10.1007/s12553-020-00508-4 · Cited by 47
Estimating runway veer-off risk using a Bayesian network with flight data
Barry, D. J. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103180 · Cited by 52
What drives the ecological quality of surface waters? A review of 11 predictive modeling tools
Visser, H., Evers, N., Bontsema, A., Rost, J., de Niet, A. C., Vethman, P., Mylius, S. D., van der Linden, A., van den Roovaart, J., van Gaalen, F., et al. (2021) · Water Research · doi:10.1016/j.watres.2021.117851 · Cited by 50
Li, Z., Zhang, C., Zhang, C., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154284 · Cited by 47
Shatnawi, A., Alkassar, H. M., Al-Abdaly, N. M., et al. (2022) · Buildings · doi:10.3390/buildings12050550 · Cited by 47
“As a result, these variables need a feature transformation to prevent larger numeric ranges from dominating smaller numeric ranges [40].”
BackgroundInfluentialNautiyal, A., & Mishra, A. K. (2022) · Environment Development and Sustainability · doi:10.1007/s10668-022-02387-3 · Cited by 47
Gleason, A. C., Ghadge, G. D., Chen, J., Sonobe, Y., & Roos, R. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0256411 · Cited by 48
“One thousand such trees were used, since this number is generally recommended as a starting point for the generation of an RFC [43].”
BackgroundMethodPredicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning
Nguyen, H., Dou, F. Y., Park, N., et al. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c00640 · Cited by 47
Genetic Algorithm Based Hyper-Parameter Tuning to Improve the Performance of Machine Learning Models
Shanthi, D. L., & Chethan, N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01537-8 · Cited by 48
Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
Yari, M., Armaghani, D. J., Maraveas, C., et al. (2023) · Applied Sciences · doi:10.3390/app13031345 · Cited by 47
Ulussever, T., Depren, S. K., Kartal, M. T., et al. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-26050-0 · Cited by 47
“The pseudo-code of the RF algorithm is described below (Kuhn and Johnson 2013): The parameter “m”, which represents the number of model build, is chosen.”
BackgroundMethodNiako, N., Melgarejo, J. D., Maestre, G. E., & Vatcheva, K. P. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02448-3 · Cited by 63
Choosing blocks for spatial cross-validation: lessons from a marine remote sensing case study
Stock, A. (2025) · Frontiers in Remote Sensing · doi:10.3389/frsen.2025.1531097 · Cited by 70
“First, we trained each model (MBR, RF, RFXY) with each complete subset, i.e., without withholding any data from the subset (Kuhn and Johnson, 2013).”
Improved Chemical Structure–Activity Modeling Through Data Augmentation
Cortés‐Ciriano, I., & Bender, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00570 · Cited by 49
Detection of elevated regions in surface images from laser beam melting processes
Jacobsmühlen, J. Z., Kleszczynski, S., Witt, G., & Merhof, D. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392275 · Cited by 47
Approaches for credit scorecard calibration: An empirical analysis
Bequé, A., Coussement, K., Gayler, R. W., & Lessmann, S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.034 · Cited by 47
Luo, G., Stone, B. L., Johnson, M. D., Tarczy-Hornoch, P. Z., Wilcox, A. B., Mooney, S. D., Sheng, X., Haug, P. J., & Nkoy, F. L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.7757 · Cited by 45
“Compared to statistical methods like logistic regression, machine learning poses less strict assumptions on distribution of data, can increase prediction/classification accuracy, in certain cases doubling it [10-12], and has won most data science competitions [13].”
MethodGrüss, A., Chagaris, D., Babcock, E. A., et al. (2018) · Marine and Coastal Fisheries · doi:10.1002/mcf2.10002 · Cited by 47
Digital Footprints of Sensation Seeking
Schoedel, R., Au, Q., Völkel, S. T., Lehmann, F., Becker, D., Bühner, M., Bischl, B., Hußmann, H., & Stachl, C. (2018) · Zeitschrift für Psychologie · doi:10.1027/2151-2604/a000342 · Cited by 46
“In order to prepare the dataset for prediction modeling, we applied a series of preprocessing steps according to Kuhn and Johnson ( 2013 ) and Schiffner et al. ( 2016 ).”
MethodNjage, P. M. K., Henri, C., Leekitcharoenphon, P., Mistou, M., Hendriksen, R. S., & Hald, T. (2018) · Risk Analysis · doi:10.1111/risa.13239 · Cited by 50
“…was such that the model input samples were randomly partitioned into 10 sets of roughly equal size, held-out samples were used to estimate accuracy, the first subset was returned to the training set, and the procedure was repeated with the second subset held out and so forth (Kuhn & Johnson, 2013).”
BackgroundMethodInfluentialLlinares, F. M., Moneva, A., & Esteve, M. (2018) · Crime Science · doi:10.1186/s40163-018-0089-1 · Cited by 49
“Additionally, to avoid results to be positively or negatively influenced by the training set composition, we used к-fold cross validation defining к = 5 subsamples (Kuhn and Johnson 2013).”
MethodHe, M., Liu, Z., Lin, Y., Wan, J., Li, J., Xu, K. L., Wang, Y., Jin, Z., Tian, J., & Xue, H. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.05.024 · Cited by 47
D’Alelio, D., Rampone, S., Cusano, L. M., Morfino, V., Russo, L., Sanseverino, N., Cloern, J. E., & Lomas, M. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-59989-y · Cited by 48
Hoffmann, G., Balcılar, M., Tognetti, V., et al. (2020) · Journal of Computational Chemistry · doi:10.1002/jcc.26376 · Cited by 47
The secret life of test smells - an empirical study on test smell evolution and maintenance
Kim, D. J., Chen, T.-H. P., & Yang, J. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-09969-1 · Cited by 47
Rafat, A., Rezanezhad, F., Quinton, W. L., Humphreys, E. R., Webster, K. L., & Cappellen, P. V. (2021) · Communications Earth & Environment · doi:10.1038/s43247-021-00184-w · Cited by 53
Tian, H., Zhu, J., He, X., et al. (2022) · Forest Ecosystems · doi:10.1016/j.fecs.2022.100037 · Cited by 46
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
