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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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“Recursive Feature Elimination is an algorithm that performs a backward selection, which avoids refitting many models at each step of the search (Kuhn and Johnson, 2013).”
BackgroundMethod
Analysis of terrain attributes in different spatial resolutions for digital soil mapping application in southeastern Brazil
Sena, N. C., Veloso, G. V., Filho, E. I. F., Francelino, M. R., & Schaefer, C. E. G. R. (2020) · Geoderma Regional · doi:10.1016/j.geodrs.2020.e00268 · Cited by 60
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“Bad data are usually caused by measurements or input errors, data corruption, but the apparent outliers can be true observations (Kuhn and Johnson, 2013).”
Background
A data driven binning strategy for the construction of insurance tariff classes
Henckaerts, R., Antonio, K., Clijsters, M., & Verbelen, R. (2018) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2018.1429300 · Cited by 68
“A better approach is to transform the continuous and spatial risk factors with many levels in categorical risk factors with fewer levels, also called binning by Kuhn and Johnson (2013).”
Background
Predicting common maternal postpartum complications: leveraging health administrative data and machine learning
Betts, K. S., Kisely, S. R., & Alati, R. (2019) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.15607 · Cited by 60
“For a thorough explanation of gradient boosted trees and boosting in general see Kuhn & Johnson (2013) and Hastie et al. (2009).”
Background
Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach
Kuo, K.-M., Talley, P. C., Huang, C., & Cheng, L. C. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0792-1 · Cited by 56
“Besides contrasting the performance of various models, we also ranked the predictor importance based on information gain and gain ratio [29].”
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Using a multivariate regression tree to analyze trade-offs between ecosystem services: Application to the main cropping area in France
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Measuring the stability of results from supervised statistical learning
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“We have used the total variation distance (TVD) to compute similarity and the Kappa statistic (see, e.g., Kuhn and Johnson 2013, chap.”
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“low proportion of false positives) [43].”
An automatic recognition system of Brazilian flora species based on textural features of macroscopic images of wood
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Psychometric and Machine Learning Approaches to Reduce the Length of Scales
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“Machine learning algorithms have been previously used to select which predictors to include in models (Kuhn & Johnson, 2013).”
BackgroundMethod
Predictive Modeling of Virus Inactivation by UV
Rockey, N. C., Henderson, J., Chin, K., Raskin, L. M., & Wigginton, K. R. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c07814 · Cited by 55
Road surface type classification based on inertial sensors and machine learning
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Deep Learning for SARS COV-2 Genome Sequences
Whata, A., & Chimedza, C. (2021) · IEEE Access · doi:10.1109/access.2021.3073728 · Cited by 60
Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice
Coppola, F., Giannini, V., Gabelloni, M., Panić, J., Defeudis, A., Monaco, S. L., Cattabriga, A., Cocozza, M. A., Pastore, L. V., Polici, M., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11050756 · Cited by 56
Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems
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“In our case, the sample size was the same as the original dataset [59], and we applied 100 repetitions.”
Method
A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace
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“Moreover, the k value is chosen as 5, which is very common in ML [71, 72].”
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“It is recommended that ntree = 1000 is a good start [23].”
Background
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“The dataset is usually divided into training and test sets for modeling purposes; the first is used in order to train and optimize the model’s hyperparameters (model selection) while the latter is to estimate the model prediction error or accuracy [41].”
Method
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Smart baseline models for solar irradiation forecasting
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“Other names, which essentially mean the same thing, are applied predictive modeling, artificial intelligence, and statistical learning (Kuhn and Johnson 2013).”
Background
Review on Nasopharyngeal Carcinoma: Concepts, methods of analysis, segmentation, classification, prediction and impact: A review of the research literature
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“two predictors is highly likely to be more effective than utilising two or more independent predictors [5].”
Background
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“Specifically, we use logistic regression (LR), which has high bias and low variance and, support vector machine, which has lowbias and high variance [13].”
BackgroundMethod
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Froemelt, A., Buffat, R., & Hellweg, S. (2019) · Journal of Industrial Ecology · doi:10.1111/jiec.12969 · Cited by 53
“The 10% left out sample was then used as a test set and to calibrate the probabilities (Kuhn & Johnson, 2013; Niculescu-Mizil & Caruana, 2005; Pedregosa et al., 2012).”
Influential
Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming
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Non-Destructive Assessment of Chicken Egg Fertility
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“While RMSE is a function of the model residuals, R2 can be understood as the fraction of the data information being explained by the model, and it is usually in close relation to correlation [102].”
Method
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Can Machines Learn Capital Structure?
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“Second, careful execution of the features engineering process has to be performed (Kuhn & Johnson, 2013).”
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“A detailed description of MLPNN can be found in Kuhn and Johnson [37] and Goodfellow et al. [27].”
A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics
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“0 ([62]; and (iv) RF; all within the R software.”
BackgroundMethod
Identification of high nature value grassland with remote sensing and minimal field data
Stenzel, S., Fassnacht, F. E., Mack, B., et al. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.11.005 · Cited by 55
A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma
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Irritability and Its Clinical Utility in Major Depressive Disorder: Prediction of Individual-Level Acute-Phase Outcomes Using Early Changes in Irritability and Depression Severity
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Assimilating optical satellite remote sensing images and field data to predict surface indicators in the Western U.S.: Assessing error in satellite predictions based on large geographical datasets with the use of machine learning
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Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population
Carretero, R. G., Vigil, L., Mora-Jiménez, I., Soguero-Ruíz, C., Barquero‐Pérez, Ó., & Ramos, J. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02132-w · Cited by 60
“We performed n-fold cross-validation, where n = 10, to obtain an average prediction accuracy [30].”
Method
Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials
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Retrieving nitrogen isotopic signatures from fresh leaf reflectance spectra: disentangling δ15N from biochemical and structural leaf properties
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Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
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Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes
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Coding and regulatory variants are associated with serum protein levels and disease
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“The reference dataset needs to be split while training the DNN model in training (data used to fit the model), validation (parameter optimization of the model) and testing datasets (unbiased evaluation of final model (Kuhn & Johnson, 2013).”
Method
Leaf area index estimations by deep learning models using RGB images and data fusion in maize
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“…dataset, i.e. a completely independent set of images that were not used during the model fine-tuning process and that provides an unbiased evaluation of the models, avoiding the possibility of model overfitting or pre-learning, masking the actual performance of the model (Kuhn & Johnson, 2013).”
Method
An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data
Vaughan, L., Zhang, M., Gu, H., Rose, J. B., Naughton, C. C., Medema, G. J., Allan, V., Roiko, A. H., Blackall, L. L., & Zamyadi, A. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.159748 · Cited by 54
“This result aligns with ﬁ ndings from previous studies with RF, which associated inclusion of unimportant variables with poorer model performance (Kuhn and Johnson, 2013; Tyralis and Papacharalampous, 2017).”
Result
A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms
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Machine learning with monotonic constraint for geotechnical engineering applications: an example of slope stability prediction
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The role of machine learning in predictive toxicology: A review of current trends and future perspectives
Ajisafe, O. M., Adekunle, Y. A., Egbon, E., Ogbonna, C. E., & Olawade, D. B. (2025) · Life Sciences · doi:10.1016/j.lfs.2025.123821 · Cited by 61
Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe
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Predicting asthma control deterioration in children
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“” To remove distributional skewedness ([34], Section 3.”
BackgroundMethod
THE MILKY WAY PROJECT: LEVERAGING CITIZEN SCIENCE AND MACHINE LEARNING TO DETECT INTERSTELLAR BUBBLES
Beaumont, C. N., Goodman, A. A., Kendrew, S., et al. (2014) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/214/1/3 · Cited by 54
Random rotation ensembles
Blaser, R., & Fryźlewicz, P. (2016) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · Cited by 54
Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.
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“While Hastie and colleagues tend to rely heavily on cross-validation in their work, others have been critical of it (Breiman, Friedman, Stone, & Olshen, 1984; Kuhn & Johnson, 2013).”
Background
Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA
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SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data
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Predictive Models of Assistance Dog Training Outcomes Using the Canine Behavioral Assessment and Research Questionnaire and a Standardized Temperament Evaluation
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Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing
Dias, S. H. B., Filgueiras, R., Filho, E. I. F., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245834 · Cited by 53
To analyse a trace or not? Evaluating the decision-making process in the criminal investigation
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“Details about the chosen statistical procedure can be found in Kuhn and Johnson [23].”
Method
When felids and hominins ruled at Olduvai Gorge: A machine learning analysis of the skeletal profiles of the non-anthropogenic Bed I sites
Arriaza, M. C., & Domínguez‐Rodrigo, M. (2016) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2016.03.005 · Cited by 53
Using Random Forest Models to Identify Correlates of a Diabetic Peripheral Neuropathy Diagnosis from Electronic Health Record Data
Dubrava, S., Mardekian, J., Sadosky, A., Bienen, E. J., Parsons, B., Hopps, M., & Markman, J. D. (2016) · Pain Medicine · doi:10.1093/pm/pnw096 · Cited by 54
Quantification of table olives' acid, bitter and salty tastes using potentiometric electronic tongue fingerprints
Marx, Í. M. G., Rodrigues, N., Dias, L. G., et al. (2017) · LWT · doi:10.1016/j.lwt.2017.01.060 · Cited by 53
Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region
Pôças, I., Gonçalves, J., Costa, P. M., Gonçalves, I., Pereira, L. S., & Cunha, M. (2017) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2017.02.013 · Cited by 55
Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors
Becker, R., & Thrän, D. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.10.044 · Cited by 52
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Automated detection and sorting of microencapsulation via machine learning
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Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches
Wang, M., Sun, R., Zhu, A., et al. (2020) · Remote Sensing · doi:10.3390/rs12061003 · Cited by 53
Probabilistic urban water demand forecasting using wavelet-based machine learning models
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Applications for solar irradiance nowcasting in the control of microgrids: A review
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“Deep learning (DL) 1 is a subset of machine learning (ML) 2,3 that has proven to be particularly effective in medical image analysis.”
Background
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“Repeated training-test splits (number of repeats: 25) were used for cross-validation [20].”
Method
Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras
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“, 2001), soft independent modelling of class analogy (SIMCA) (Wold and Sjöström, 1977), or training some other algorithm to differentiate training set data from a background of random data (Hastie et al., 2009; Kuhn and Johnson, 2013).”
BackgroundMethod

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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