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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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“Random forests then uses the models to generate prediction (Kuhn & Johnson, 2013).”
Revisiting acoustic correlates of pharyngealization in Jordanian and Moroccan Arabic: Implications for formal representations
Al‐Tamimi, J. (2017) · Laboratory Phonology Journal of the Association for Laboratory Phonology · doi:10.5334/labphon.19 · Cited by 68
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“The specific objectives were: (1) to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for tree diameter growth modelling based on tree size, competition, site condition and climate factors; and (2) to identify the contributions of different factors to the individual tree diameter growth.”
BackgroundMethodResultInfluential
Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability
Zheng, X., Zhong, D., He, Y., & Zhou, G. (2019) · Infectious Diseases of Poverty · doi:10.1186/s40249-019-0612-y · Cited by 65
Cross validation methods: Analysis based on diagnostics of thyroid cancer metastasis
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“As this difference decreases, the bias of the technique becomes smaller [12].”
Background
Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed
Anmala, J., & Turuganti, V. (2021) · Water Environment Research · doi:10.1002/wer.1642 · Cited by 65
“This algorithm predicts the target variable using the independent variables (Breiman et al., 1984; James et al., 2013; Kuhn & Johnson, 2013; Ma, 2018; Witten et al., 2016).”
Method
A review of downscaling methods of satellite-based precipitation estimates
Abdollahipour, A., Ahmadi, H., & Aminnejad, B. (2021) · Earth Science Informatics · doi:10.1007/s12145-021-00669-4 · Cited by 65
Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers
Valentino, M., Běhal, J., Bianco, V., Itri, S., Mossotti, R., Fontana, G. D., Battistini, T., Stella, E., Miccio, L., & Ferraro, P. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152708 · Cited by 67
Automatic Cardiac Arrhythmia Classification Using Residual Network Combined With Long Short-Term Memory
Kim, Y. K., Lee, M., Song, H. S., & Lee, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3181276 · Cited by 70
A hybrid machine-learning model for predicting the waste generation rate of building demolition projects
Cha, G.-W., Moon, H. J., & Kim, Y. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134096 · Cited by 67
A review of deep learning methods for pixel-level crack detection
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Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System
Shyu, H.-Y., Castro, C. J., Bair, R. A., Lu, Q., & Yeh, D. H. (2023) · ACS Environmental Au · doi:10.1021/acsenvironau.2c00072 · Cited by 67
Intimate insight: MDMA changes how people talk about significant others
Baggott, M. J., Kirkpatrick, M. G., Bedi, G., & de Wit, H. (2015) · Journal of Psychopharmacology · doi:10.1177/0269881115581962 · Cited by 69
“Predicting dosing condition from individual words We used recursive feature elimination (Guyon and Elisseeff, 2006; Kuhn and Johnson, 2013) to build ensembles with decreasing numbers of predictor variables.”
Method
Environmental Controls, Emergent Scaling, and Predictions of Greenhouse Gas (GHG) Fluxes in Coastal Salt Marshes
Abdul‐Aziz, O. I., Ishtiaq, K. S., Tang, J., Moseman‐Valtierra, S. M., Kroeger, K. D., Gonneea, M. E., Mora, J. W., & Morkeski, K. (2018) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2018jg004556 · Cited by 66
“The PLSR model parameters were robustly estimated using the SIMPLS algorithm (de Jong, 1993) and a 10-fold cross-validation (Kuhn & Johnson, 2013).”
Method
Machine learning and multi-sensor based modelling of woody vegetation in the Molopo Area, South Africa
Ludwig, M., Morgenthal, T., Detsch, F., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.12.019 · Cited by 65
Effect of Drought-Induced Salinization on Wetland Methane Emissions, Gross Ecosystem Productivity, and Their Interactions
Chamberlain, S. D., Hemes, K. S., Eichelmann, E., et al. (2019) · Ecosystems · doi:10.1007/s10021-019-00430-5 · Cited by 65
Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture
Rafferty, C., Johnson, K., O'Mahony, J., Burgoyne, B., Rea, R., & Balss, K. M. (2020) · Biotechnology Progress · doi:10.1002/btpr.2977 · Cited by 63
Self-citation
An Intrusion Detection System for Multi-class Classification Based on Deep Neural Networks
Toupas, P., Chamou, D., Giannoutakis, K. M., et al. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00206 · Cited by 64
“features with a constant value for all the examples), since they could not provide additional statistical information, [26], for our ML algorithm to be able to ”learn” from these features.”
Background
Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
Epstein, D. H., Tyburski, M., Kowalczyk, W. J., Burgess-Hull, A. J., Phillips, K. A., Curtis, B. L., & Preston, K. L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0234-6 · Cited by 64
Mass wasting susceptibility assessment of snow avalanches using machine learning models
Choubin, B., Borji, M., Hosseini, F. S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-75476-w · Cited by 64
“A higher occurrence frequency of a variable indicates its higher importance because the RFE is a backward select method 108 , which the lower important variables are removed from modeling and higher important variables are reminded 109 .”
Method
Predictive Performance Models in Long-Distance Runners: A Narrative Review
Alvero‐Cruz, J. R., Carnero, E. Á., García, M. A. G., Alacid, F., Correas-Gómez, L., Rosemann, T. J., Nikolaidis, P. Τ., & Knechtle, B. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17218289 · Cited by 73
“…of multicollinearity are the following: improvement in the sample design by extracting the maximum information from the observed variables, elimination of the variables suspected of causing multi-collinearity and, ﬁnally, in the case of having few observations, increasing the sample size [59].”
Background
A Machine Learning decision-making tool for extubation in Intensive Care Unit patients
Fabregat, A., Magret, M., Ferré, J. A., Vernet, A., Guasch, N., Rodríguez, A. H., Gómez, J., & Bodí, M. A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105869 · Cited by 66
Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models
Namous, M., Hssaisoune, M., Pradhan, B., Lee, C.-W., Alamri, A. M., Elaloui, A., Edahbi, M., Krimissa, S., Eloudi, H., Ouayah, M., et al. (2021) · Water · doi:10.3390/w13162273 · Cited by 65
“Consequently, all trees’ prediction results are averaged to build the final set of model predictions [39], and the data that are not involved in the analysis are defined as the out-of-bag (OOB) error.”
BackgroundMethod
A Conversation-Driven Approach for Chatbot Management
Santos, G. A., de Andrade, G. G., Silva, G. R. S., Duarte, F. C. M., da Costa, J. P. C. L., & de Sousa, R. T. (2022) · IEEE Access · doi:10.1109/access.2022.3143323 · Cited by 68
Measurements and determinants of extreme multidimensional energy poverty using machine learning
Abbas, K., Butt, K. M., Xu, D., Ali, M. S., Baz, K., Kharl, S. H., & Ahmed, M. (2022) · Energy · doi:10.1016/j.energy.2022.123977 · Cited by 69
Calculating amphibole 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.2020.105469 · Cited by 64
“Therefore, the second regression step, regression of matrix Y on the principal components of matrix X, coincides perfectly with the best application of PCR, which requires that the directions in which the predictors have the largest variation are the exact directions associated with the corresponding response variable…”
BackgroundMethod
Estimation of total soluble solids in grape berries using a hand‐held NIR spectrometer under field conditions
Urraca-Valle, R., Sanz-García, A., Tardáguila, J., et al. (2015) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.7470 · Cited by 64
Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth
Sawada, Y., SATO, T. A., Nagaya, M., Saito, C., Yoshihara, H., Banno, C., Matsumoto, Y., Matsuda, Y., Yoshikai, K., Sawada, T., et al. (2021) · Reproductive BioMedicine Online · doi:10.1016/j.rbmo.2021.05.002 · Cited by 66
Building prediction models with grouped data: A case study on the prediction of turnover intention
Yuan, S., Kroon, B., & Kramer, A. (2021) · Human Resource Management Journal · doi:10.1111/1748-8583.12396 · Cited by 80
Imbalanced classification: A paradigm‐based review
Feng, Y., Zhou, M., & Tong, X. (2021) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11538 · Cited by 81
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
Prentice, R. M., Villoslada, M., Ward, R. D., et al. (2021) · Remote Sensing · doi:10.3390/rs13183669 · Cited by 63
“solution to solve a complex computational problem [65].”
Background
Deep GMDH Neural Networks for Predictive Mapping of Mineral Prospectivity in Terrains Hosting Few but Large Mineral Deposits
Parsa, M., Carranza, E. J. M., & Ahmadi, B. (2021) · Natural Resources Research · doi:10.1007/s11053-021-09984-5 · Cited by 64
“The bias–variance tradeoff of predictive models should also be controlled by estimating quantitative measures of bias and variance (Kuhn & Johnson, 2013).”
BackgroundMethodInfluential
LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination
Saberi, A. N., Belahcen, A., Sobra, J., & Vaimann, T. (2022) · IEEE Access · doi:10.1109/access.2022.3195939 · Cited by 69
“LOOCV is also a special case of K-fold CV, where K is the number of observations [35], [36].”
Background
A novel two-phase clustering-based under-sampling method for imbalanced classification problems
Farshidvard, A., Hooshmand, F., & MirHassani, S. . (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119003 · Cited by 61
Predicting land surface temperature and examining its relationship with air pollution and urban parameters in Bengaluru: A machine learning approach
Suthar, G., Kaul, N., Khandelwal, S., et al. (2024) · Urban Climate · doi:10.1016/j.uclim.2024.101830 · Cited by 63
Sensory intensity assessment of olive oils using an electronic tongue
Veloso, A. C. A., Dias, L. G., Rodrigues, N., et al. (2015) · Talanta · doi:10.1016/j.talanta.2015.08.071 · Cited by 63
“For this purpose, data was divided into K subsets and K models were fitted, each time considering K-1 subsets, as the training set, leaving out one of the subsets for the internal validation, to compute the predictive error for the obtained model [39].”
Method
Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
Alonso‐Sarría, F., Hernández, C. M., Díaz, M. A. R., García, F. C., & Gomariz‐Castillo, F. (2015) · Land Degradation and Development · doi:10.1002/ldr.2447 · Cited by 63
“In this case, it is better to use a threshold that maximizes prediction accuracy (Kuhn & Johnson, 2014).”
Method
Comparison and integration of lidar and photogrammetric point clouds for mapping pre-fire forest structure
Filippelli, S. K., Lefsky, M. A., & Rocca, M. E. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.01.029 · Cited by 65
Is image-based CAPTCHA secure against attacks based on machine learning? An experimental study
Alqahtani, F. H., & Alsulaiman, F. A. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101635 · Cited by 64
Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression
van Bronswijk, S. C., Bruijniks, S. J. E., Lorenzo‐Luaces, L., DeRubeis, R. J., Lemmens, L. H. J. M., Peeters, F. P. M. L., & Huibers, M. J. H. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1823029 · Cited by 59
INCIDENTAL LEARNING OF COLLOCATIONS FROM MEANINGFUL INPUT
Vu, D. V., & Peters, E. (2021) · Studies in Second Language Acquisition · doi:10.1017/s0272263121000462 · Cited by 68
“A variable with near-zero variance should be removed from a model (Kuhn & Johnson, 2013).”
Method
CO and NOx emissions prediction in gas turbine using a novel modeling pipeline based on the combination of deep forest regressor and feature engineering
Coelho, L. D. S., Ayala, H. V. H., & Mariani, V. C. (2023) · Fuel · doi:10.1016/j.fuel.2023.129366 · Cited by 61
Sensory attributes shaping consumers’ willingness-to-pay for newly developed processed meat products with natural compounds and a reduced level of nitrite
Hung, Y., & Verbeke, W. (2017) · Food Quality and Preference · doi:10.1016/j.foodqual.2017.02.017 · Cited by 63
Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation
Baldo, N., Manthos, E., & Miani, M. (2019) · Applied Sciences · doi:10.3390/app9173502 · Cited by 64
Comparative analysis of phenomenological growth models applied to epidemic outbreaks
Bürger, R., 1 CI2MA and Departamento de Ingeniería Matemática, F. D. C. F. Y. M., Chowell, G., & Lara-Díaz, L. Y. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019212 · Cited by 61
A Comparative Study of Predictive Approaches for Load Forecasting in Smart Buildings
Hadri, S., NaitMalek, Y., Najib, M., Bakhouya, M., Fakhri, Y., & Elaroussi, M. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.09.458 · Cited by 66
Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning
Vulova, S., Meier, F., Fenner, D., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3019696 · Cited by 61
Soil property maps with satellite images at multiple scales and its impact on management and classification
Silvero, N. E. Q., Demattê, J. A. M., Vieira, J. D. S., et al. (2021) · Geoderma · doi:10.1016/j.geoderma.2021.115089 · Cited by 61
An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification
Seng, Z., Kareem, S. A., & Varathan, K. D. (2021) · IEEE Access · doi:10.1109/access.2021.3088414 · Cited by 67
“Table 2 presents the kernel equations of the kernel trick [54], [57], [58], where γ is the scaling parameter, coef is a coefficient, and d is the degree of the polynomial.”
Background
Machine learning-based constitutive models for cement-grouted coal specimens under shearing
Li, G., Sun, Y., & Qi, C. (2021) · International Journal of Mining Science and Technology · doi:10.1016/j.ijmst.2021.08.005 · Cited by 61
Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network
Sung, J., Han, S., Park, H. S., Cho, H.-M., Hwang, S., Park, J. W., & Youn, I. (2021) · Sensors · doi:10.3390/s22010053 · Cited by 68
“evaluate the relationship between each input variable and the target variable [30].”
BackgroundMethod
Soft-computing modeling and multiresponse optimization for nutrient removal process from municipal wastewater using microalgae
Hossain, S. M. Z., Sultana, N., Jassim, M. S., Coskuner, G., Hazin, L. M., Razzak, S. A., & Hossain, M. M. (2022) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2021.102490 · Cited by 61
Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms
Bennemann, B., Schwartz, B., Giesemann, J., & Lutz, W. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2022.17 · Cited by 62
“Repeating the cross validation leads to a more precise result, 47 so we conducted this procedure for a more adequate comparison.”
Method
Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques
Zhou, Y., Wu, W., Wang, H., et al. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3164140 · Cited by 61
Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms
Sakhaee, A., Gebauer, A., Ließ, M., & Don, A. (2022) · SOIL · doi:10.5194/soil-8-587-2022 · Cited by 62
“Optimising the C parameter has a crucial impact on SVR performance since a high C can lead to overfitting, while a low C can cause under fitting (Kuhn and Johnson, 2013).”
Background
Random forest and spatial cross-validation performance in predicting species abundance distributions
Mushagalusa, C. A., Fandohan, A. B., & Kakaï, R. G. (2024) · ENVIRONMENTAL SYSTEMS RESEARCH · doi:10.1186/s40068-024-00352-9 · Cited by 77
“It is also able to handle scenarios where the number of predictors significantly exceeds the number of observations (Kuhn and Johnson 2013; Scornet 2016; Zurell et al. 2016; Zhang et al. 2020).”
Background
Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model
Fienen, M. N., Nolan, B. T., & Feinstein, D. T. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.11.023 · Cited by 61
“A popular technique for tuning the statistical model to avoid overfitting is k-fold cross validation (e.g. Kuhn and Johnson, 2013; Hastie et al., 2009).”
BackgroundMethodInfluential
A time-efficient optimization for robust image watermarking using machine learning
Abdelhakim, A. M., & Abdelhakim, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.002 · Cited by 60
Visualizing Uncertainty and Alternatives in Event Sequence Predictions
Guo, S., Du, F., Malik, S., Koh, E., Kim, S., Liu, Z., Kim, Y., Hongyuan Zha, B., & Cao, N. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300803 · Cited by 56
Object based burned area mapping with random forest algorithm
Çömert, R., Matcı, D. K., & Avdan, U. (2019) · International Journal of Engineering and Geosciences · doi:10.26833/ijeg.455595 · Cited by 61
Per-pixel land cover accuracy prediction: A random forest-based method with limited reference sample data
Ebrahimy, H., Mirbagheri, B., Matkan, A. A., et al. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.11.024 · Cited by 60
Factors Affecting Nitrate Concentrations in Stream Base Flow
Wherry, S. A., Tesoriero, A. J., & Terziotti, S. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c02495 · Cited by 64
Making Sense of Model Generalizability: A Tutorial on Cross-Validation in R and Shiny
Song, Q. C., Tang, C., & Wee, S. (2021) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245920947067 · Cited by 64
“…to the systematic difference between the population parameter (e.g., ρ2) and the cross-validated estimate (e.g., RCV.Avg 2), and variance refers to the uncertainty (or expected change) in the cross-validated estimates when different data partitions are used (e.g., Kuhn & Johnson, 2013, p. 70).”
MethodInfluential
Monitoring peatland water table depth with optical and radar satellite imagery
Räsänen, A., Tolvanen, A., & Kareksela, S. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102866 · Cited by 60
A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling
Cao, Z., Nan, Z., Jianan, H., et al. (2023) · Earth system science data · doi:10.5194/essd-15-3905-2023 · Cited by 60
“0 decision tree (Kuhn and Johnson, 2013) was trained on the information of soil clusters in the subregions and then applied to predict soil clusters for all regions outside the subregions on the QTP on a cell basis, based on the same environmental factors used in spatial clustering.”
Relationship between built form and energy performance of office buildings in a severe cold Chinese region
Tian, W., Song, Y., Zuo, J., et al. (2016) · Building Simulation · doi:10.1007/s12273-016-0314-3 · Cited by 60
Self-training for multi-target regression with tree ensembles
Levatić, J., Ceci, M., Kocev, D., et al. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.02.014 · Cited by 60
“001) [55].”
Successful classification of experimental bone surface modifications (BSM) through machine learning algorithms: a solution to the controversial use of BSM in paleoanthropology?
Domínguez‐Rodrigo, M. (2018) · Archaeological and Anthropological Sciences · doi:10.1007/s12520-018-0684-9 · Cited by 60
“Cut-marked bones subjected to trampling can also show these striae. because of its applicability to most ML tests is crossvalidation (Kuhn and Johnson 2013).”
BackgroundMethodInfluential
A genetic algorithm‐based stacking algorithm for predicting soil organic matter from vis–NIR spectral data
Tsakiridis, N., Tziolas, N. V., Theocharis, J. B., & Zalidis, G. C. (2018) · European Journal of Soil Science · doi:10.1111/ejss.12760 · Cited by 61
“Multiple rule-based models can be combined (committees) and the final predictions may be adjusted using known errors on the training set with a small number of neighbours for each unknown sample (Kuhn & Johnson, 2013).”
Background
Automated plaque analysis for the prognostication of major adverse cardiac events.
van Assen, M., Varga-Szemes, Á., Schoepf, U., Duguay, T., Hudson, H., Egorova, S., Johnson, K., Pierre, S. S., Zaki, B., Oudkerk, M., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.04.013 · Cited by 57
Self-citation
TitAnt
Cao, S., Yang, X., Chen, C., Zhou, J., Li, X., & Qi, Y. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352126 · Cited by 82
“0 [33, 50] is revised version of C4.”
Background
Climate change decreases the cooling effect from postfire albedo in boreal North America
Potter, S., Solvik, K. K., Erb, A. M., Goetz, S. J., Johnstone, J. F., Mack, M. C., Randerson, J. T., Román, M. O., Schaaf, C., Turetsky, M. R., et al. (2019) · Global Change Biology · doi:10.1111/gcb.14888 · Cited by 62
“We followed the methods of Kuhn and Johnson (2013), which involves the following iterative procedure: (a) calculate the correlation matrix of all variables, (b) determine the two predictors with the largest correlations, (c) remove the predictor from this set of two that has the largest mean…”
Method
Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow
Reis, G. B., da Silva, D. D., Filho, E. I. F., et al. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112625 · Cited by 60
Application of soft computing techniques in tunnelling and underground excavations: state of the art and future prospects
Shreyas, S. K., & Dey, A. (2019) · Innovative Infrastructure Solutions · doi:10.1007/s41062-019-0234-z · Cited by 59
Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”
Mancini, A., Vito, L., Marcelli, E., Piangerelli, M., Leone, R. D., Pucciarelli, S., & Merelli, E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03566-7 · Cited by 59
Understanding and managing the interactions of impacts from nature-based recreation and climate change
Monz, C., Gutzwiller, K. J., Hausner, V. H., Brunson, M. W., Buckley, R., & Pickering, C. M. (2020) · AMBIO · doi:10.1007/s13280-020-01403-y · Cited by 60
A Deep Learning Inspired Belief Rule-Based Expert System
Islam, R. U., Hossain, M. S., & Andersson, K. (2020) · IEEE Access · doi:10.1109/access.2020.3031438 · Cited by 63
“comparing the performance among different methods [38].”
Background
Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data
Pedersen, C. F., Andersen, M. Ø., Carreon, L. Y., & Eiskjær, S. P. (2020) · Global Spine Journal · doi:10.1177/2192568220967643 · Cited by 67
“The basic idea is that merged together, the predictions of several trees should be closer to the true value than any single tree.(29,30) Boosted trees are ensembles of very simple decision trees referred to as weak learners.”
Background
Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study
Schultebraucks, K., Sijbrandij, M., Galatzer‐Levy, I. R., Mouthaan, J., Olff, M., & van Zuiden, M. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100297 · Cited by 61
“Model building and evaluation was performed with R package ‘caret ’ (Kuhn and Johnson, 2013) in 3.6.0 using RStudio Version 1.2.1335.”
MethodInfluential
Opening the black box: Uncovering the leader trait paradigm through machine learning
Doornenbal, B. M., Spisak, B. R., & van der Laken, P. A. (2021) · The Leadership Quarterly · doi:10.1016/j.leaqua.2021.101515 · Cited by 65
An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions
Lyu, Y., Li, H., Sayagh, M., Jiang, Z. M., & Hassan, A. E. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3447876 · Cited by 60
Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning
Hounkpatin, O., Bossa, A. Y., Yira, Y., Igué, A. M., & SINSIN, B. A. (2021) · Geoderma Regional · doi:10.1016/j.geodrs.2021.e00444 · Cited by 61
The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review
Nelon, C., Myers, O., & Hall, A. J. (2022) · Journal of Composite Materials · doi:10.1177/00219983211037048 · Cited by 62
“11,20”
Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression
Ghunimat, D. M., Alzoubi, A. E., Alzboon, A., et al. (2022) · Asian Journal of Civil Engineering · doi:10.1007/s42107-022-00495-z · Cited by 59
“It is able to determine the pattern relating inputs to outputs with a supervised learning technique addressed as backpropagation (Kuhn & Johnson, 2013; Rosenblatt, 1961; Rumelhart et al., 1986).”
Method
Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction
Kumar, C., Walton, G., Santi, P. M., & Huillca, C. A. L. (2025) · Remote Sensing · doi:10.3390/rs17020213 · Cited by 62
Multidomain Verification of Dynamic Signatures Using Local Stability Analysis
Pirlo, G., Cuccovillo, V., Díaz, M., et al. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2443050 · Cited by 59
“5 decision tree classifier [41] was built using the information related to the d ∗ (v) domain of representation of the set of segments (corresponding to Sn∗(v) of the reference signatures).”
Method
Patterns and magnitude of flow alteration in California, USA
Zimmerman, J. K. H., Carlisle, D. M., May, J. T., Klausmeyer, K. R., Grantham, T. E., Brown, L. R., & Howard, J. K. (2017) · Freshwater Biology · doi:10.1111/fwb.13058 · Cited by 58
“Nevertheless, given the general robustness of RF to overfitting (Kuhn & Johnson, 2013) and the large numbers of observations in calibration sets, the approach balances the risk of overfitting with obtaining the best predictive performance for the models as possible.”
Background
Predicting reference soil groups using legacy data: A data pruning and Random Forest approach for tropical environment (Dano catchment, Burkina Faso)
Hounkpatin, O., Schmidt, K., Stumpf, F., Forkuor, G., Behrens, T., Scholten, T., Amelung, W., & Welp, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-28244-w · Cited by 60
Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images
Ortiz-Ramón, R., Hernández, M. D. C. V., González‐Castro, V., Makin, S., Armitage, P. A., Aribisala, B. S., Bastin, M. E., Deary, I. J., Wardlaw, J. M., & Moratal, D. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.02.006 · Cited by 59
Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study
Johnson, A. K., Yang, F., Gollarahalli, S., Banerjee, T., Abrams, D. M., Jonassaint, J., Jonassaint, C. R., & Shah, N. (2019) · JMIR mhealth and uhealth · doi:10.2196/13671 · Cited by 60
“Then, the posterior distribution can be used to make predictions [34].”
Method
The compatibility of theoretical frameworks with machine learning analyses in psychological research
Elhai, J. D., & Montag, C. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.05.002 · Cited by 70
Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits
Yoosefzadeh-Najafabadi, M., Tulpan, D., & Eskandari, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250665 · Cited by 61
Hominin skeletal part abundances and claims of deliberate disposal of corpses in the Middle Pleistocene
Egeland, C. P., Domínguez‐Rodrigo, M., Pickering, T. R., Menter, C. G., & Heaton, J. L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718678115 · Cited by 57
A comparison of model selection methods for prediction in the presence of multiply imputed data
Thảo, L. T. P., & Geskus, R. B. (2018) · Biometrical Journal · doi:10.1002/bimj.201700232 · Cited by 59
Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation
Ling, B., Goodin, D. G., Raynor, E. J., & Joern, A. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00142 · Cited by 58
“However, the model prediction accuracy may decrease with an increase in model complexity (Kuhn and Johnson, 2013).”
Background
Predictive factors of quality of life among medical students: results from a multicentric study
Miguel, A. D. Q. C., Tempski, P. Z., Kobayasi, R., Mayer, F. B., & Martins, M. A. (2021) · BMC Psychology · doi:10.1186/s40359-021-00534-5 · Cited by 58
“Statistical analysis We started with a descriptive and visual exploratory analysis of all variables to evaluate their frequency, percentage and near-zero variance for categorical variables, distribution for numeric variables, and their corresponding missing value patterns [67].”
Method
Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR
Navarese, E. P., Zhang, Z., Kubica, J., Andreotti, F., Farinaccio, A., Bartorelli, A. L., Bedogni, F., Rupji, M., Tomai, F., Giordano, A., et al. (2021) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2021.03.024 · Cited by 60
GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus
Islam, R., Devnath, M. K., Samad, M. D., & Kadry, S. M. J. A. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100442 · Cited by 59
Sensitivity Analysis by the PROMETHEE-GAIA method: Algorithms evaluation for COVID-19 prediction
Moreira, M. Â. L., Gomes, C. F. S., dos Santos, M., et al. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.052 · Cited by 58
Regional-scale prediction of pluvial and flash flood susceptible areas using tree-based classifiers
Kaiser, M. K., Günnemann, S., & Disse, M. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128088 · Cited by 58

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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