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Applied Predictive Modeling

Kuhn, Max, Johnson, Kjell·2013·Springer eBooks

DOI 10.1007/978-1-4614-6849-3

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“For instance, Kuhn and Johnson (2013) trialed several mitigation strategies to avoid overfitting in machine learning (e.g., model tuning, data splitting and resampling) and proposed a general model building approach, incorporating model tuning and model evaluation with the ultimate goal of finding…”
BackgroundMethod
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“ii) As the size of the training set is small (< 2000), Monte Carlo validation [44] is used”
Method
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“For validation of model outcomes, we applied a 10-fold cross-validation repeated five times as recommended by Kuhn and Johnson [34].”
Method
Intelligent Approach Based on Random Forest for Safety Risk Prediction of Deep Foundation Pit in Subway Stations
Zhou, Y., Li, S., Zhou, C., & Luo, H. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000796 · Cited by 145
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“Strengths of our study include the use of a systematic approach to model comparison dealing with limitations from previous studies [35, 70] including (1) use of different datasets to train and test models, (2) optimisation of tuning parameters [24, 30], (3) calibration [18] and (4) decision curve…”
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“Balancing can improve predictions to the minority class by forcing the model to learn the relationships that predict the minority class, as well as the majority class (Fernández et al., 2009; He & Garcia, 2009; Kuhn & Johnson, 2013).”
Background
Three‐dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
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“…model, multiple stepwise regression model (Friedman et al., 2001), least angle regression model (Efron et al., 2004), and elastic net model (Kuhn & Johnson, 2013; Zou & Hastie, 2005)] and four nonlinear models [cubist model (Quinlan, 1992), boosted tree model (Friedman, 2001), bagged tree…”
Method
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“…rankings so that otherwise irrelevant features with high correlations to informative predictors get disproportionately large importance values 18,65,] or conversely, if there are many truly relevant predictors but they are highly correlated, their importance measures get diluted and will be…”
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Early Predictions of Movie Success: The Who, What, and When of Profitability
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“Such regularization allows the model to select features that are more informative for the prediction and reduce the impact of collinearity [16].”
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Diagnosing root causes of intermittent slow queries in cloud databases
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“However, the author [46] suggested the cross-validation technique as a dominant technique to overcome the imbalanced class distribution.”
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“The wrapper approach is one of the applied techniques for FS (Kuhn and Johnson 2013) and GA is a good example of wrapper FS methodologies.”
BackgroundMethodInfluential
Evaluation of portable near-infrared spectroscopy for organic milk authentication
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Method
Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification
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“These metrics are suitable for evaluating the accuracy of predicted classes and the probabilities of an event [Kuhn and Johnson, 2013].”
MethodInfluential
FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees
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“Support vector machines (SVM) and random forests (RF, Kuhn & Johnson, 2013) are known to be highly robust against overfitting and thus should provide a stronger challenge to FFTs.”
Background
Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty
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Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data
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“Consequently, since the squared residuals are not used, large outliers have a limited effect on the regression equation and simultaneously samples with small residuals have no effect on the regression equation (Kuhn & Johnson, 2013).”
Method
Froth image analysis by use of transfer learning and convolutional neural networks
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Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor
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Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
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REFORMS: Consensus-based Recommendations for Machine-learning-based Science
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“Not using a held-out test set is a textbook error in ML ( 158 ).”
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Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion
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“We choose a value of k = 5, which is very common in ML [93, 94].”
Background
A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive–behavioral versus psychodynamic therapy for mild to moderate adult depression
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“10-fold CV is recommended based on its good bias and variance properties in small samples (Kuhn & Johnson, 2013).”
BackgroundMethodInfluential
Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making
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“The models were assessed by the Balanced Accuracy [81] metric defined in (Table 3):”
BackgroundMethodInfluential
A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning
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“For this paper, the chosen value for k was 10, as this is a typical choice [38].”
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“The rfe() function implemented in caret (Kuhn, 2008, Kuhn and Johnson, 2013) incorporates a resampling step that deals with concerns due to selection bias (Ambroise and McLachlan, 2002) and improper use of resampling (Kuhn and Johnson, 2013).”
BackgroundMethodInfluential
The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance
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Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations
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Quantitative Assessment of the Impact of Fluorine Substitution on P-Glycoprotein (P-gp) Mediated Efflux, Permeability, Lipophilicity, and Metabolic Stability
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Self-citation
Development and improvement of occupant behavior models towards realistic building performance simulation: A review
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Automobile insurance classification ratemaking based on telematics driving data
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Conditional Inference Trees and Random Forests
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Flood susceptibility prediction using tree-based machine learning models in the GBA
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Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging
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Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States
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Crop NDVI Monitoring Based on Sentinel 1
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“For more information about them, see the publication by Kuhn and Johnson [41].”
BackgroundMethod
A rule-based model for Seoul Bike sharing demand prediction using weather data
Easwaramoorthy, S. V., & Cho, Y. (2020) · European Journal of Remote Sensing · doi:10.1080/22797254.2020.1725789 · Cited by 118
Machine learning in rare disease
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“There are multiple approaches that can be deployed to maximally use the available data when generating training and evaluation datasets, for example, leave-p -out cross-validation, leave-one-out cross-validation, k -fold cross-validation and Monte-Carlo random subsampling cross-validation 83 .”
Method
Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework
Dreyfus, P.-A., Psarommatis, F., May, G., et al. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1976433 · Cited by 119
“Moreover, keeping features uncorrelated with the output can reduce the model’s overall effectiveness (Kuhn and Johnson 2013; Kim and Kang 2019).”
Background
Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests
Terryn, L., Calders, K., Bartholomeus, H. M., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.112912 · Cited by 119
Advances and applications of machine learning and deep learning in environmental ecology and health
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Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling
Cortés‐Ciriano, I., Murrell, D. S., van Westen, G. J. P., Bender, A., & Malliavin, T. E. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-014-0049-z · Cited by 117
“Model generation 210 Descriptors with a variance close to zero were removed 211 with the function RemoveNearZeroVarianceFeatures from 212 the R package camb using a cut-off value equal to 30/1 213 [41,49,51].”
Method
Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability
Maynard, J. J., & Levi, M. R. (2016) · Geoderma · doi:10.1016/j.geoderma.2016.09.024 · Cited by 120
“However, this approach only represents model performance for one pair of testing and training sets and can result in accuracy metrics with high variance when sample sizes are low or when sample class sizes are highly imbalanced (Kuhn and Johnson, 2013).”
BackgroundMethodInfluential
Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran
Taghizadeh‐Mehrjardi, R., Mahdianpari, M., Mohammadimanesh, F., Behrens, T., Toomanian, N., Scholten, T., & Schmidt, K. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114552 · Cited by 117
Radiomics in radiation oncology—basics, methods, and limitations
Lohmann, P., Bousabarah, K., Hoevels, M., & Treuer, H. (2020) · Strahlentherapie und Onkologie · doi:10.1007/s00066-020-01663-3 · Cited by 117
Improving predictions of hydrological low-flow indices in ungaged basins using machine learning
Worland, S. C., Farmer, W. H., & Kiang, J. E. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.12.021 · Cited by 115
“Machine learningealso referred to as statistical learning, datadriven modeling, and computational intelligenceerefers to a set of statistical methods that are optimized for predictive performance through a cross-validated parameter tuning process (Hastie et al., 2013; Kuhn and Johnson, 2013).”
Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes
Guðmundsdóttir, V., Zaghlool, S. B., Emilsson, V., Aspelund, T., Ilkov, M., Gudmundsson, E. F., Jonsson, S. M., Zilhäo, N. R., Lamb, J. R., Suhre, K., et al. (2020) · Diabetes · doi:10.2337/db19-1070 · Cited by 121
“Statistical Analysis Box-Cox transformation was applied on the protein data (21).”
Method
Evaluating prediction model performance
Cabot, J., & Ross, E. (2023) · Surgery · doi:10.1016/j.surg.2023.05.023 · Cited by 117
Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency
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“This is often achieved by limiting the number of terms or interactions used for learning data structures [27].”
Background
DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models
Aliferi, A., Ballard, D., Gallidabino, M. D., Thurtle, H., Barron, L., & Court, D. S. (2018) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2018.09.003 · Cited by 116
“3 in combination with the caret package [86] was employed in order to test fourteen regression methods: ordinary linear (LM), partial least squares (PLS), ridge regression (Ridge), elastic net (Enet), lasso regression (LASSO), bagging multivariate adaptive regression splines (BagMARS), k nearest neighbours (KNN), extr…”
Method
Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis
Regueira‐Iglesias, A., Balsa‐Castro, C., Blanco‐Pintos, T., & Tomás, I. (2023) · Molecular Oral Microbiology · doi:10.1111/omi.12434 · Cited by 146
LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0
Rossi, M., & Reichenbach, P. (2016) · Geoscientific model development · doi:10.5194/gmd-9-3533-2016 · Cited by 115
“The technique has been largely used to estimate errors and uncertainties associated with clas-siﬁcation models (among the others, Kuhn and Kjell, 2013).”
Method
Anthropogenic marine debris over beaches: Spectral characterization for remote sensing applications
Acuña-Ruz, T., Uribe, D., Taylor, R. D., Amézquita, L., Guzmán, M. C., Merrill, J., Martínez, P., Voisin, L., & Mattar, C. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.08.008 · Cited by 114
Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers
REEVE, J. P., Böhmig, G. A., Eskandary, F. A., et al. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15351 · Cited by 115
Integrated decision strategies for skin sensitization hazard
Strickland, J. A., Zang, Q., Kleinstreuer, N., et al. (2016) · Journal of Applied Toxicology · doi:10.1002/jat.3281 · Cited by 114
“For LOOCV, the training and test set substances were combined, and the performance of the model was evaluated against every substance in the dataset when it appears in an external test set on its own (Kuhn and Johnson, 2013).”
Method
A review of prognostics and health management of machine tools
Baur, M., Albertelli, P., & Monno, M. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05202-3 · Cited by 114
Best Practices in Supervised Machine Learning: A Tutorial for Psychologists
Pargent, F., Schoedel, R., & Stachl, C. (2023) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231162559 · Cited by 131
“Our focus is exclusively on supervised ML (James et al., 2021; Kuhn & Johnson, 2013).”
Advancing Network Security in Industrial IoT: A Deep Dive into AI-Enabled Intrusion Detection Systems
Shahin, M., Maghanaki, M., Hosseinzadeh, A., & Chen, F. F. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102685 · Cited by 119
Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood
Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2022) · Psychological Medicine · doi:10.1017/s0033291720002718 · Cited by 108
“Data were preprocessed using R package caret (Breiman, 1996; Kuhn, 2008; Kuhn & Johnson, 2013).”
Method
Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests
Ludwig, N., Feuerriegel, S., & Neumann, D. (2015) · Journal of Decision System · doi:10.1080/12460125.2015.994290 · Cited by 111
“These are robust to errors resulting from the inclusion of predictors that do not contribute to the model by performing feature selection (Kuhn & Johnson, 2013).”
Method
Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique
Ahmed, M. H., & Lin, L.-S. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126213 · Cited by 109
"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing
Xiao, B., Imel, Z. E., Georgiou, P., Atkins, D. C., & Narayanan, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143055 · Cited by 123
“[37] Here the empathy prediction model was trained on all but one counselor’s sessions and then the model predicted the empathy of the one, left-out counselor’s sessions based on the words generated by the ASR transcripts for those sessions, meaning that predictions of empathy are fully automatic and independent to th…”
Method
Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting
Vock, D. M., Wolfson, J. A., Bandyopadhyay, S., Adomavičius, G., Johnson, P. E., Vazquez‐Benitez, G., & O’Connor, P. J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.009 · Cited by 126
“0 decision trees [40] use the information gain metric instead of the Gini impurity to make decisions on how to split each node.”
Evaluation of Android Malware Detection Based on System Calls
Dimjašević, M., Atzeni, S., Ugrina, I., & Rakamarić, Z. (2016) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/2875475.2875487 · Cited by 116
“Hence, every application’s execution is completely independent of executions of all the other analyzed applications.”
Background
Trends in the Contribution of Emergency Departments to the Provision of Hospital-Associated Health Care in the USA
Marcozzi, D. E., Carr, B. G., Liferidge, A. T., Baehr, N. E., & Browne, B. J. (2017) · International Journal of Health Services · doi:10.1177/0020731417734498 · Cited by 121
Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring
Zhou, B., Okin, G. S., & Zhang, J. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111521 · Cited by 116
Mapping the Risk Terrain for Crime Using Machine Learning
Wheeler, A. P., & Steenbeek, W. (2020) · Journal of Quantitative Criminology · doi:10.1007/s10940-020-09457-7 · Cited by 134
Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran
Akbarian, M., Saghafian, B., & Golian, S. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129480 · Cited by 115
Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism
Scherfler, C., Göbel, G., Müller, C. F., Nocker, M., Wenning, G. K., Schocke, M. F. H., Poewe, W. H., & Seppi, K. (2016) · Neurology · doi:10.1212/wnl.0000000000002518 · Cited by 112
Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
Notton, G., Voyant, C., Fouilloy, A., Duchaud, J.-L., & Nivet, M. L. (2019) · Applied Sciences · doi:10.3390/app9010209 · Cited by 112
Calculating biotite formula from electron microprobe analysis data using a machine learning method based on principal components regression
Li, X., Zhang, C., Behrens, H., et al. (2020) · Lithos · doi:10.1016/j.lithos.2020.105371 · Cited by 111
Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies
Lombard, M. A., Bryan, M. S., Jones, D. K., Bulka, C. M., Bradley, P. M., Backer, L. C., Focazio, M. J., Silverman, D. T., Toccalino, P. L., Argos, M., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c05239 · Cited by 114
Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features
Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2020) · Electronics · doi:10.3390/electronics9010144 · Cited by 110

Authors: Kuhn, M., & Johnson, K.
Venue: Springer eBooks
Publisher: Springer New York
Published: 2013
DOI: 10.1007/978-1-4614-6849-3
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

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