Applied Predictive Modeling
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Using the same dataset for training and testing the model is a textbook example of overfitting, which leads to overoptimistic performance estimates.
“[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]
The study used 5-fold cross-validation with 3 repetitions to select optimal lambda and alpha parameters and to obtain a cross-validation estimate of model performance.
“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]
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.
“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
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“This is the main reason why a training-testing approach is used when dealing with ML problems [162, 163].”
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733 · see citing works
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149 · see citing works
“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).”
BackgroundCross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689 · see citing works
“All the available training data can be used to fit a new 1) model with which to make a single set of final predictions (Kuhn and Johnson 2013).”
BackgroundMethodOn hyperparameter optimization of machine learning algorithms: Theory and practice
Yang, L., & Shami, A. A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.061 · Cited by 3,347 · see citing works
“model parameters; while the other, named hyper-parameters, cannot be directly estimated from data learning and must be set before training a ML model because they dene the architecture of a ML model [4]. Hyper-parameters are the parameters that are used to either congure a ML model (e.g., the penalty parameter Cin a s…”
BackgroundGraph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
“Although it should be recommendable, we did not use the area under ROC curve as quality measure, nor develop cutoff tuning (Kuhn and Johnson, 2013), because some classifiers do not give probabilistic output.”
MethodA Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
Mienye, I. D., & Sun, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3207287 · Cited by 1,438 · see citing works
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387 · see citing works
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988 · see citing works
“During the process, the panelists consulted extensively the broad literature on machine learning and predictive model in particular [16-18].”
Global predictions of primary soil salinization under changing climate in the 21st century
Hassani, A., Azapagic, A., & Shokri, N. (2021) · Nature Communications · doi:10.1038/s41467-021-26907-3 · Cited by 1,069 · see citing works
Variable selection strategies and its importance in clinical prediction modelling.
Chowdhury, M. Z. I., & Turin, T. C. (2020) · Family Medicine and Community Health · doi:10.1136/fmch-2019-000262 · Cited by 1,011 · see citing works
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Zhong, S., Zhang, K., Bagheri, M., Burken, J. G., Gu, A. Z., Li, B., Ma, X., Marrone, B. L., Ren, Z. J., Schrier, J., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c01339 · Cited by 974 · see citing works
Tomislav Hengl, A., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M., Shepherd, K. D., Sila, A., MacMillan, R. A., de Jesus, J. S. M., Tamene, L. D., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125814 · Cited by 942 · see citing works
“Common machine learning algorithms are: artificial neural networks, support vector machines, classification and regression trees, and random forests [31, 35].”
BackgroundMethodValavi, R., Guillera-Arroita, G., Lahoz-Monfort, J., & Elith, J. (2021) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 798 · see citing works
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809 · see citing works
“Note that the predicted probability p is not calibrated and should be interpreted with care as it is not reflective of the true underlying probability of the sample applied to CNNs (Guo et al., 2017; Kuhn and Johnson, 2013).”
BackgroundTyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 793 · see citing works
A review of machine learning applications in wildfire science and management
Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. (2020) · Environmental Reviews · doi:10.1139/er-2020-0019 · Cited by 883 · see citing works
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762 · see citing works
“2 MORE BOOKS • theoretical RL books (Bertsekas, 2012; Bertsekas and Tsitsiklis, 1996; Szepesvári, 2010) • an operations research oriented RL book (Powell, 2011) • an edited RL book (Wiering and van Otterlo, 2012) • Markov decision processes (Puterman, 2005) • deep learning (Deng and Dong, 2014) • machine learning (Bis…”
BackgroundCross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676 · see citing works
A molecular portrait of microsatellite instability across multiple cancers
Cortés‐Ciriano, I., Lee, S., Park, W., Kim, T., & Park, P. J. (2017) · Nature Communications · doi:10.1038/ncomms15180 · Cited by 651 · see citing works
Abdi, A. M. (2019) · GIScience & Remote Sensing · doi:10.1080/15481603.2019.1650447 · Cited by 645 · see citing works
“In the case of SVM, RF and Xgboost, optimization was performed using randomized sampling of all hyper-parameter combinations up to a speci fi ed number of iterations (Kuhn and Johnson 2013b; Bergstra and Bengio 2012).”
MethodMapping high resolution National Soil Information Grids of China
Liu, F., Wu, H., Zhao, Y.-G., Li, C., Yang, J., Song, X., Shi, Z., Zhu, A., & Zhang, G. (2021) · 中国科学通报：英文版 · doi:10.1016/j.scib.2021.10.013 · Cited by 659 · see citing works
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.06.002 · Cited by 482 · see citing works
“Hyperparameters are determined by finding the optimal value for a model across multiple unknown data sets by using a optimization procedure such as CV or Bayesian optimization while parameters of parametric models are estimated when fitting them to the data (Kuhn & Johnson, 2013).”
MethodOrdered quantile normalization: a semiparametric transformation built for the cross-validation era
Peterson, R., & Cavanaugh, J. (2019) · Journal of Applied Statistics · doi:10.1080/02664763.2019.1630372 · Cited by 592 · see citing works
“This method is called a ‘preprocessing’ technique [12], and it essentially forgoes the assumption of linearity between the outcome and the covariate, opting instead for the premise of a linear relationship between the outcome and the transformed value of the covariate (which in many cases may be more plausible).”
MethodMeyer, H., Reudenbach, C., Tomislav Hengl, A., Katurji, M., & Nauß, T. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.12.001 · Cited by 602 · see citing works
Data driven prediction models of energy use of appliances in a low-energy house
Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.01.083 · Cited by 559 · see citing works
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547 · see citing works
Ribeiro, M. H. D. M., & Coelho, L. D. S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105837 · Cited by 528 · see citing works
Emergency department triage prediction of clinical outcomes using machine learning models
Raita, Y., Goto, T., Faridi, M. K., Brown, D., Camargo, C. A., & Hasegawa, K. (2019) · Critical Care · doi:10.1186/s13054-019-2351-7 · Cited by 531 · see citing works
Lamichhane, S., Kumar, L., & Wilson, B. R. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.05.031 · Cited by 520 · see citing works
Feature selection techniques for machine learning: a survey of more than two decades of research
Theng, D. P., & Bhoyar, K. K. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02010-5 · Cited by 522 · see citing works
Gradient boosting machine for modeling the energy consumption of commercial buildings
Touzani, S., Granderson, J., & Fernandes, S. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.11.039 · Cited by 508 · see citing works
Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.108815 · Cited by 509 · see citing works
“Recent software developments have simplified the application of machine learning algorithms (e.g. for R see Kuhn & Johnson, 2013).”
BackgroundMethodInfluentialState-of-the-art review of soft computing applications in underground excavations
Zhang, W., Zhang, R., Wu, C., Goh, A. T. C., Lacasse, S., Liu, Z., & Liu, H. (2019) · Geoscience Frontiers · doi:10.1016/j.gsf.2019.12.003 · Cited by 484 · see citing works
“In this al-gorithm, using multiple DTs with the same distribution to set up a forest to train and predict the sample data (Kuhn and Johnson, 2013; Zhang et al., 2019b,d).”
MethodFinding Optimal Normalizing Transformations via bestNormalize
Ryan, A. P. (2021) · The R Journal · doi:10.32614/rj-2021-041 · Cited by 482 · see citing works
“Another increasingly popular application of normalization occurs in applied regression settings with highly skewed distributions of the covariates (Kuhn and Johnson, 2013).”
BackgroundA comparison of machine learning techniques for customer churn prediction
Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2015.03.003 · Cited by 471 · see citing works
“The dataset has been used in numerous publications [22, 23, 24, 25, 26, 27, 28].”
MethodModelling and mapping soil organic carbon stocks in Brazil
Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G. R., & Filho, E. I. F. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.01.007 · Cited by 469 · see citing works
A review of uncertainty analysis in building energy assessment
Tian, W., Heo, Y., de Wilde, P., Li, Z., Yan, D., Park, C. S., Feng, X., & Augenbroe, G. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.05.029 · Cited by 460 · see citing works
“A meta package of Classification and Regression Training (caret) [203] is particularly useful because it combines a large number of machine learning methods to easily create several machine learning models and compare their predictive performance.”
BackgroundComparison of feature importance measures as explanations for classification models
Saarela, M., & Jauhiainen, S. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04148-9 · Cited by 479 · see citing works
Treatment selection in depression.
Cohen, Z. D., & DeRubeis, R. J. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084746 · Cited by 435 · see citing works
Machine learning for precision medicine
MacEachern, S. J., & Forkert, N. D. (2020) · Genome · doi:10.1139/gen-2020-0131 · Cited by 464 · see citing works
“…learning subtypes, is 9 beyond the scope of this review and the interested reader is referred to textbooks 10 such as Applied Predictive Modelling (Kuhn & Johnson, 2013) or review papers 11 including Angermueller et al (Angermueller, Pärnamaa, Parts, & Stegle, 2016), Ching 12 et al (Ching et al.,…”
MethodSelecting appropriate machine learning methods for digital soil mapping
Khaledian, Y., & Miller, B. A. (2019) · Applied Mathematical Modelling · doi:10.1016/j.apm.2019.12.016 · Cited by 450 · see citing works
Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods
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Artificial intelligence and machine learning for medical imaging: A technology review
Montero, A. M. B., Javaid, U., Valdés, G., Nguyen, D. T. D., Desbordes, P., Macq, B. M. M., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.016 · Cited by 481 · see citing works
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Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C. R., Hernández, J., Corvalán, P., & Koch, B. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.07.028 · Cited by 439 · see citing works
“Methods that use this approach include leave-one-out crossvalidation, k-fold cross-validation, as well as repeated splits in training and validation samples (Kuhn & Johnson, 2013).”
MethodHan, L., Yang, G., Dai, H., Xu, B., Yang, H. F., Feng, H., Li, Z., & Yang, X. (2019) · Plant Methods · doi:10.1186/s13007-019-0394-z · Cited by 431 · see citing works
Young, B. A., Hall, A., Pilon, L., Gupta, P., & Sant, G. (2019) · Cement and Concrete Research · doi:10.1016/j.cemconres.2018.09.006 · Cited by 414 · see citing works
“Support vector machines (SVMs) Support vector machines (SVMs) are a family of regression models that are effective for nonlinear problems [20,21,24,25].”
BackgroundMethodInfluentialDormann, C. F., Calabrese, J. M., Guillera‐Arroita, G., Matechou, E., Bahn, V., Bartoń, K. A., Beale, C. M., Ciuti, S., Elith, J., Gerstner, K., et al. (2018) · Ecological Monographs · doi:10.1002/ecm.1309 · Cited by 403 · see citing works
“Statistical analyses rarely oer a single solution, both because the limited 38 amount of data allows for several plausible combinations of predictors, and because 39 dierent modelling approaches are available for statistical analysis (e.g. Hastie et al., 40 2009; Kuhn and Johnson, 2013).”
MethodUP-Fall Detection Dataset: A Multimodal Approach
Martínez-Villaseñor, L., Ponce-Espinosa, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Penafort-Asturiano, C. J. (2019) · Sensors · doi:10.3390/s19091988 · Cited by 404 · see citing works
Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390 · see citing works
“Predictive modeling is defined as the process of developing a mathematical tool or model that generates an accurate prediction [24].”
MethodMachine learning for predicting soil classes in three semi-arid landscapes
Brungard, C., Boettinger, J., Duniway, M., Wills, S., & Edwards, T. C. (2015) · Geoderma · doi:10.1016/j.geoderma.2014.09.019 · Cited by 352 · see citing works
“4 Covariate selection 289 290 Recursive feature elimination (Guyon et al., 2002; Kuhn and Johnson, 2013) was used to identify 291 an optimal subset of covariates from the set of all available covariates (covariate set 1+2) for each area 292 (Fig.”
MethodInfluentialScime, L., & Beuth, J. (2018) · Additive manufacturing · doi:10.1016/j.addma.2018.11.010 · Cited by 372 · see citing works
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M., & Qian, Q. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.09.001 · Cited by 391 · see citing works
Soil quality both increases crop production and improves resilience to climate change
Qiao, L., Wang, X., Smith, P., Fan, J., Lu, Y., Emmett, B. A., Li, R., Dorling, S. R., Chen, H., Liu, S., et al. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01376-8 · Cited by 384 · see citing works
Computational design optimization of concrete mixtures: A review
DeRousseau, M. A., Kasprzyk, J. R., & Srubar, W. V. (2018) · Cement and Concrete Research · doi:10.1016/j.cemconres.2018.04.007 · Cited by 364 · see citing works
“Full descriptions and best practices for these methods are thoroughly discussed in Kuhn and Johnson [47].”
MethodStudy of cardiovascular disease prediction model based on random forest in eastern China
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“While we can inspect the residuals for any systematic deviations of the model fit to the data, it is also possible to quantify the error of the model fit to the data using performance metrics (Kuhn & Johnson, 2013) .”
MethodZhou, J., Li, E., Yang, S. L., Wang, M., Shi, X., Yao, S. Z., & Mitri, H. S. (2019) · Safety Science · doi:10.1016/j.ssci.2019.05.046 · Cited by 337 · see citing works
Machine learning methods in sport injury prediction and prevention: a systematic review
Eetvelde, H. V., Mendonça, L. D. M., Ley, C., Seil, R., & Tischer, T. (2021) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-021-00346-x · Cited by 391 · see citing works
“ML is the study of algorithms that can automatically learn from data to make new decisions [23].”
BackgroundAn Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., & Zeineddine, H. (2019) · IEEE Access · doi:10.1109/access.2019.2927266 · Cited by 341 · see citing works
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Variable Importance Plots—An Introduction to the vip Package
Brandon, M. G., & Bradley, C. B. (2020) · The R Journal · doi:10.32614/rj-2020-013 · Cited by 341 · see citing works
“For categorical predictors, a different method based on standard statistical tests (e.g., t-tests and ANOVAs) can be employed; see Kuhn and Johnson (2013) for details.”
MethodStock market prediction using machine learning classifiers and social media, news
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01839-w · Cited by 336 · see citing works
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Determinants of Default in P2P Lending
Serrano‐Cinca, C., Nieto, B. G., & López-Palacios, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139427 · Cited by 334 · see citing works
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MyShake: A smartphone seismic network for earthquake early warning and beyond
Kong, Q., Allen, R. M., Schreier, L., et al. (2016) · Science Advances · doi:10.1126/sciadv.1501055 · Cited by 319 · see citing works
Overfitting, Model Tuning, and Evaluation of Prediction Performance
Montesinos-López, O. A., Montesinos‐López, A., & Crossa, J. L. (2022) · Springer eBooks · doi:10.1007/978-3-030-89010-0_4 · Cited by 319 · see citing works
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Feature Engineering and Selection: A Practical Approach for Predictive Models
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Modelling species presence‐only data with random forests
Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2021) · Ecography · doi:10.1111/ecog.05615 · Cited by 300 · see citing works
Predicting failure in the U.S. banking sector: An extreme gradient boosting approach
Carmona, P., Climent, F., & Momparler, A. (2018) · International Review of Economics & Finance · doi:10.1016/j.iref.2018.03.008 · Cited by 296 · see citing works
Digital mapping of soil carbon fractions with machine learning
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Safety leading indicators for construction sites: A machine learning approach
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A genome-wide association study of serum proteins reveals shared loci with common diseases
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Utilizing big data analytics for information systems research: challenges, promises and guidelines
Müller, O., Junglas, I. A., Brocke, J. V., & Debortoli, S. (2016) · European Journal of Information Systems · doi:10.1057/ejis.2016.2 · Cited by 282 · see citing works
“Such explanations can take many forms, including comparing and contrasting predictions with existing theories and related explanatory empirical studies (Shmueli & Koppius, 2011), ‘ replaying ’ predictions for extreme or typical cases (Robnik-Sikonja & Kononenko, 2008), performing a sensitivity analysis for selected mo…”
BackgroundMethodInfluentialSoil Property and Class Maps of the Conterminous United States at 100‐Meter Spatial Resolution
Ramcharan, A., Tomislav Hengl, A., Nauman, T., Brungard, C., Waltman, S. W., Wills, S., & Thompson, J. A. (2018) · Soil Science Society of America Journal · doi:10.2136/sssaj2017.04.0122 · Cited by 272 · see citing works
“titioning scheme was repeated 10 times to calculate the averaged performance metrics. For each of the six numeric soil properties, we derived the 2 value, mean error, mean absolute error, R and RMSE (Kuhn and Johnson, 2013). All equations for crossvalidation metrics are provided in the supplementary materials. Indepen…”
InfluentialHou, S., Liu, Y., & Yang, Q. (2021) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2021.05.004 · Cited by 274 · see citing works
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Mosavi, A., Hosseini, F. S., Choubin, B., Goodarzi, M., Dineva, A. A., & Sardooi, E. R. (2020) · Water Resources Management · doi:10.1007/s11269-020-02704-3 · Cited by 269 · see citing works
“The RFE is a backward select method (Kuhn and Johnson 2013) which the main concept of it is based on eliminating the unimportance variables.”
Knoll, L., Breuer, L., & Bach, M. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.03.045 · Cited by 269 · see citing works
Qi, C., & Tang, X. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.02.028 · Cited by 265 · see citing works
Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients
Churpek, M. M., Yuen, T. C., Winslow, C. J., Robicsek, A. A., Meltzer, D. O., Gibbons, R. D., & Edelson, D. P. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201406-1022oc · Cited by 258 · see citing works
“In addition, because cardiac arrest comprises a small proportion of all patients in the combined outcome, the importance of these patients in model estimation was then increased by using frequency weights by factors of 10, 25, 50, 75, and 100 to determine if this further improved model accuracy for cardiac arrest with…”
MethodBand, S. S., Janizadeh, S., Pal, S. C., Saha, A., Chakrabortty, R., Melesse, A. M., & Mosavi, A. (2020) · Remote Sensing · doi:10.3390/rs12213568 · Cited by 259 · see citing works
Neaimeh, M., Salisbury, S., Hill, G. A., Blythe, P. T., Scoffield, D., & Francfort, J. E. (2017) · Energy Policy · doi:10.1016/j.enpol.2017.06.033 · Cited by 254 · see citing works
“The 10 resampled estimates of performance are summarised usually with the mean and standard error (Kuhn and Johnson, 2013).”
BackgroundMethodElectrocardiogram screening for aortic valve stenosis using artificial intelligence
Cohen-Shelly, M., Attia, Z. I., Friedman, P. A., Ito, S., Essayagh, B. A., Ko, W.-Y., Murphree, D. H., Michelena, H. I., Enriquez-Sarano, M., Carter, R. E., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab153 · Cited by 252 · see citing works
Formation lithology classification using scalable gradient boosted decision trees
Dev, V. A., & Eden, M. R. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.06.001 · Cited by 243 · see citing works
A machine learning framework for assessing post-earthquake structural safety
Zhang, Y., Burton, H. V., Sun, H., & Shokrabadi, M. (2017) · Structural Safety · doi:10.1016/j.strusafe.2017.12.001 · Cited by 241 · see citing works
Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
Zhou, J., Li, E., Wei, H., Li, C., Qiao, Q., & Armaghani, D. J. (2019) · Applied Sciences · doi:10.3390/app9081621 · Cited by 240 · see citing works
“To make the Cubist model easy to interpret, it can be regarded as a model that consists of many rules, in which each rule is attached a relevant multivariate linear model [32].”
MethodInfluentialRawal, A., McCoy, J., Rawat, D. B., Sadler, B. M., & Amant, R. S. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133846 · Cited by 243 · see citing works
Zhou, J. P., Shi, X., Du, K., et al. (2016) · International Journal of Geomechanics · doi:10.1061/(asce)gm.1943-5622.0000817 · Cited by 239 · see citing works
Optimizing ensembles of small models for predicting the distribution of species with few occurrences
Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12957 · Cited by 234 · see citing works
“It uses tuning functions from the caret r- package (to tune GBM, ANN, GAM, MARS, GLM and CTA; Kuhn, 2008; Kuhn & Johnson, 2013), enmeval r- package (to tune Max.”
MethodA review of machine learning in obesity
DeGregory, K. W., Kuiper, P. K., DeSilvio, T., Pleuss, J. D., Miller, R., Roginski, J. W., Fisher, C. B., Harness, D., Viswanath, S. E., Heymsfield, S. B., et al. (2018) · Obesity Reviews · doi:10.1111/obr.12667 · Cited by 222 · see citing works
“This creates even more problems if there are noisy variables, because the splitting into compartments can be dictated by the noisy variables (45).”
BackgroundDeep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues
Al‐Qurishi, M., Khalid, T., & Souissi, R. (2021) · IEEE Access · doi:10.1109/access.2021.3110912 · Cited by 243 · see citing works
- 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 · 2012: 1
- 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
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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.
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