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Applied Predictive Modeling
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“[L1.1] No test set. Using the same dataset for training and testing the model is a textbook example of overfitting, which leads to overoptimistic performance estimates.54”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [54]
In the citing paper
“Following recommendations by Kuhn and Johnson,31 we used 5-fold cross-validation with 3 repetitions to select the optimal λ (shrinkage) and α (mixing) parameters for each elastic net model. This also allowed us to obtain a cross-validation estimate of model performance.”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [31]
In the citing paper
“Random forests can naturally be adapted to fit the unbalanced data framework by down-sampling the majority class and growing each tree on a more balanced data set (Chen et al., 2004; Kuhn and Johnson, 2013).”
From A random forest guided tour (2016) · cites this paper as Kuhn and Johnson, 2013
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Ten quick tips for biomarker discovery and validation analyses using machine learning
Díaz‐Uriarte, R., de Lope, E. G., Giugno, R., Fröhlich, H., Nazarov, P. V., Nepomuceno-Chamorro, I. A., Rauschenberger, A., & Glaab, E. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010357 · Cited by 49
Machine learning in biosignals processing for mental health: A narrative review
Sajno, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., & Riva, G. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1066317 · Cited by 59
“Each repetition is called a fold; the final validation score will be the average of all the fold validations (Kuhn and Johnson, 2013; Shalev-Shwartz and Ben-David, 2014; Goodfellow et al., 2016; Chollet, 2017).”
BackgroundMethodRezaei, M., Mousavi, S. R., Rahmani, A., Zeraatpisheh, M., Rahmati, M., Pakparvar, M., Abadi, V. A. J. M., Seuntjens, P., & Cornelis, W. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107821 · Cited by 46
Using automated analysis to assess middle school students' competence with scientific argumentation
Wilson, C. D., Haudek, K. C., Osborne, J. F., Bracey, Z. B., Cheuk, T., Donovan, B., Stuhlsatz, M. A. M., Santiago, M. M., & Zhaı, X. (2023) · Journal of Research in Science Teaching · doi:10.1002/tea.21864 · Cited by 49
Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment
Serafin, R., Koyuncu, C., Xie, W., Huang, H., Glaser, A. K., Reder, N. P., Janowczyk, A. R., True, L. D., Madabhushi, A., & Liu, J. (2023) · The Journal of Pathology · doi:10.1002/path.6090 · Cited by 43
“This nested CV was performed 200 times to determine an AUC (average and SD) [42].”
MethodArtificial intelligence and customers’ intention to use robo-advisory in banking services
Piotrowski, D., & Orzeszko, W. (2023) · Equilibrium Quarterly Journal of Economics and Economic Policy · doi:10.24136/eq.2023.031 · Cited by 46
“Each model in the ensemble is then used to generate a prediction for a new bootstrap sample and these predictions are averaged to give the bagged model’s prediction (Kuhn & Johnson, 2013).”
MethodEshaghi, A., Riyahi-Alam, S., Saeedi, R., Roostaei, T., Nazeri, A., Aghsaei, A., Doosti, R., Ganjgahi, H., Bodini, B., Shakourirad, A., et al. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.001 · Cited by 47
“We used the Caret package inside R version 3.1.1 (Kuhn and Johnson, 2013) and the Shogun toolbox version 3.2.0 inside C++.”
MethodInfluentialGarcía, F. C., & Alonso‐Sarría, F. (2015) · Remote Sensing · doi:10.3390/rs70404651 · Cited by 48
“Another advantage is that the independence assumption causes a significant reduction in computing time [66].”
BackgroundLudwig, A., Meyer, H., & Nauß, T. (2016) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2016.03.003 · Cited by 46
Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model
António, N., de Almeida, A., & Nunes, L. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00-11 · Cited by 54
“As recognized by [29], how predictors are encoded can have a significant impact in the model performance.”
BackgroundComparing Technical and Fundamental Indicators in Stock Price Forecasting
Beyaz, E., Tekiner, F., Zeng, X., et al. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00262 · Cited by 46
“A standard way to ensure robustness is to use K-fold cross validation [28].”
MethodUAV-based canopy textures assess changes in forest structure from long-term degradation
Bourgoin, C., Betbeder, J., Couteron, P., Blanc, L., Dessard, H., Oszwald, J., Roux, R. L., Cornu, G., Reymondin, L., Mazzei, L., et al. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106386 · Cited by 50
Dóra, S. A., Szabó, G., Bertalan, L., Varga, Z., Enyedi, P., & Szabó, S. (2020) · Remote Sensing · doi:10.3390/rs12152397 · Cited by 50
Personalized models for facial emotion recognition through transfer learning
Rescigno, M., Spezialetti, M., & Rossi, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09405-4 · Cited by 47
Automated species identification of frog choruses in environmental recordings using acoustic indices
Brodie, S., Allen‐Ankins, S., Towsey, M., Roe, P., & Schwarzkopf, L. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106852 · Cited by 47
A multi-class classification model for supporting the diagnosis of type II diabetes mellitus
Kuo, K.-M., Talley, P. C., Kao, Y.-H., & Huang, C. (2020) · PeerJ · doi:10.7717/peerj.9920 · Cited by 49
Joyce, J. B., Grant, C. W., Liu, D., MahmoudianDehkordi, S., Kaddurah‐Daouk, R., Skime, M. K., Biernacka, J. M., Frye, M. A., Mayes, T. L., Carmody, T. J., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01632-z · Cited by 48
Predicting outcomes: Is there utility in risk scores?
Wijeysundera, D. N. (2015) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/s12630-015-0537-2 · Cited by 48
Meyer, N., Meyer, H., Welp, G., et al. (2018) · Geoderma · doi:10.1016/j.geoderma.2018.02.031 · Cited by 45
Landslide Geometry Reveals its Trigger
Rana, K., Öztürk, U., & Malik, N. (2021) · Geophysical Research Letters · doi:10.1029/2020gl090848 · Cited by 44
“…and can be achieved for a split sq of node q if we maximize the decrease in the quantity Δθ(sq) = Gq − ρrqGr − ρlqGl; where ρrq (ρlq) are the ratio of the number of data points in daughter nodes r (l) to the total number of points in the parent node q (Kuhn & Johnson, 2013; Zhang & RANA ET AL.”
Comparison of machine learning methods for the classification of cardiovascular disease
Hagan, R., Gillan, C. J., & Mallett, F. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100606 · Cited by 54
“As the table shows, one of the weaknesses of this dataset is that there are relatively few records, and even none for the various patho-physiologies associated with AV block, meaning that the UCI dataset exhibits severe imbalance [14].”
BackgroundLobo, A., García, E., Barroso, G., Martí, D., Fernández-Turiel, J. L., & Ibáñez, J. (2021) · Remote Sensing · doi:10.3390/rs13163258 · Cited by 45
Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning
Minařík, R., Langhammer, J., & Lendzioch, T. (2021) · Remote Sensing · doi:10.3390/rs13234768 · Cited by 47
Bampoulas, A., Pallonetto, F., Mangina, E., & Finn, D. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118947 · Cited by 43
A review on Machine learning aspect in physics and mechanics of glasses
Singh, J., & Singh, S. (2022) · Materials Science and Engineering B · doi:10.1016/j.mseb.2022.115858 · Cited by 49
Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2022) · Measurement · doi:10.1016/j.measurement.2022.111706 · Cited by 45
City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning
Vulova, S., Rocha, A. D., Meier, F., Nouri, H., Schulz, C., Soulsby, C., Tetzlaff, D., & Kleinschmit, B. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113487 · Cited by 48
“Another benefit of using a data-driven approach is the flexible integration of diverse and novel datasets (Kuhn and Johnson, 2013).”
BackgroundMethodResultInfluentialVPD-based models of dead fine fuel moisture provide best estimates in a global dataset
Rodrígues, M., de Dios, V. R., Sil, Â. F., Camprubí, À. C., & Fernandes, P. M. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109868 · Cited by 48
“With SVM, we sought to minimize the effect of outliers or highly influential observations on the regression equations (Kuhn and Johnson, 2013).”
Knierim, K. J., Blondes, M. S., Masterson, A. L., et al. (2024) · Science Advances · doi:10.1126/sciadv.adp8149 · Cited by 45
“For RF models, explanatory variables are generally more important when they are used earlier in tree building or used many times ( 19 ).”
BackgroundMelcher, M., Scharl, T., Spangl, B., Luchner, M., Cserjan‐Puschmann, M., Bayer, K., Leisch, F., & Striedner, G. (2015) · Biotechnology Journal · doi:10.1002/biot.201400790 · Cited by 46
“An often applied strategy is to systematically remove variables such that the pairwise correlation of the remaining predictors does not exceed a (more or less arbitrary) limit ρ 0 [24].”
BackgroundModel Comparison for Breast Cancer Prognosis Based on Clinical Data
Boughorbel, S., Al‐Ali, R., & Elkum, N. B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146413 · Cited by 45
“Resampling techniques are used for the tuning of model parameters [18, 19].”
MethodShabangu, F. W., Yemane, D. G., Stafford, K. M., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172705 · Cited by 45
Factors that affect the nearshore aggregations of Antarctic krill in a biological hotspot
Bernard, K. S., Cimino, M. A., Fraser, W. R., et al. (2017) · Deep Sea Research Part I Oceanographic Research Papers · doi:10.1016/j.dsr.2017.05.008 · Cited by 45
“no patterns were observed between residuals and covariates), we considered the predictor variable with the largest absolute value of the t-statistic to contribute most to the model's fit of observed data (Kuhn and Johnson, 2013).”
MethodSage, K. M., Johnson, T. L., Teglas, M., Nieto, N., & Schwan, T. (2017) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0006047 · Cited by 47
Harzalli, U., Rodrigues, N., Veloso, A. C. A., et al. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.12.016 · Cited by 45
“All statistical analysis were performed using the Subselect (Cadima et al., 2004, 2012; Kuhn and Johnson, 2013) and MASS (Venables and Ripley, 2002) packages of the open source statistical program R (version 2.15.1), at a 5% significance level.”
MethodInfluentialMachine learning based prediction of depression among type 2 diabetic patients
Khalil, R. M., & Al-Jumaily, A. A. (2017) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske.2017.8258766 · Cited by 48
Heim, R. H. J., Wright, I. J., Chang, H., et al. (2018) · Plant Pathology · doi:10.1111/ppa.12830 · Cited by 45
“However, it does not provide information about the origin of an error (Kuhn & Johnson, 2013).”
BackgroundMayr, M. J., Vanselow, K. A., & Samimi, C. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.04.022 · Cited by 44
Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons
Béjaoui, B., Ottaviani, E., Barelli, E., et al. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.08.041 · Cited by 45
Gendered behavior as a disadvantage in open source software development
Vedres, B., & Vásárhelyi, O. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0202-z · Cited by 45
Machine Learning in Python for Weather Forecast based on Freely Available Weather Data
Abrahamsen, E. B., Brastein, O. M., & Lie, B. (2018) · Linköping electronic conference proceedings · doi:10.3384/ecp18153169 · Cited by 47
“Another way to reduce the risk of over-fitting is to apply a regularization method (Goodfellow et al., 2016; Kuhn and Johnson, 2013).”
MethodYoon, J., Kim, H., Lee, Y., & Sim, S. (2019) · Materials · doi:10.3390/ma12172678 · Cited by 45
“To enhance the reliability of the ANN model, five-fold cross validation is used [51,52].”
MethodSilva, B. P. C., Silva, M. L. N., Avalos, F. A. P., de Menezes, M. D., & Curi, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-50376-w · Cited by 45
Habib, M., Aljarah, I., Faris, H., & Mirjalili, S. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-32-9990-0_9 · Cited by 45
Interpreting machine learning of topological quantum phase transitions
Zhang, Y., Ginsparg, P., & Kim, E.-A. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023283 · Cited by 52
“As is well-known in the machine learning literature [31], the more expressive the machine learning architectures are, the more opaque are their decision making criteria.”
BackgroundPredicting Postsecondary Enrollment With Secondary Student Engagement Data
Fraysier, K., Reschly, A. L., & Appleton, J. J. (2020) · Journal of Psychoeducational Assessment · doi:10.1177/0734282920903168 · Cited by 45
Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment
Stamate, D., Smith, R., Tsygancov, R., Vorobev, R. S., Langham, J., Stahl, D. R., & Reeves, D. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49186-4_26 · Cited by 48
Improving facies prediction by combining supervised and unsupervised learning methods
Ippolito, M., Ferguson, J., & Jenson, F. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.108300 · Cited by 45
Alipour, M., Sander-Tavallaey, S., Andersson, A. M., & Brandell, D. (2022) · ChemPhysChem · doi:10.1002/cphc.202100829 · Cited by 46
“[39] The absolute valued Pearson correlation coefficient, as the most commonly used ranking criterion in the filter methods, was employed to select the most relevant features correlated to the target values.”
MethodA Tool for Predicting Regulatory Approval After Phase II Testing of New Oncology Compounds
DiMasi, J. A., Hermann, J., Twyman, K., Kondru, R., Stergiopoulos, S. G., Getz, K. A., & RACKOFF, W. R. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.194 · Cited by 44
Deep multistage multi-task learning for quality prediction of multistage manufacturing systems
Yan, H., Sergin, N. D., Brenneman, W. A., et al. (2021) · Journal of Quality Technology · doi:10.1080/00224065.2021.1903822 · Cited by 44
“Finally, we present an overview of the proposed methodology compared to existing manufacturing methodology such as stream of variation (denoted as “SoV”) (Shi 2006), predictive modeling (denoted as “Predictive”) (Kuhn and Johnson 2013), and multitask learning (denoted as “MTL”) (Obozinski, Taskar, and Jordan 2006) in…”
MethodCross‐item learning for volatile demand forecasting: An intervention with predictive analytics
Chuang, H. H., Chou, Y., & Oliva, R. (2021) · Journal of Operations Management · doi:10.1002/joom.1152 · Cited by 49
Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine
Arslan, E., Schulz, J., & Rai, K. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188588 · Cited by 50
Modelling and prediction of wind damage in forest ecosystems of the Sudety Mountains, SW Poland
Pawlik, Ł., & Harrison, S. P. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.151972 · Cited by 43
Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
Ageenko, A., Hansen, L. C., Lyng, K. L., et al. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11060324 · Cited by 44
Maloney, K. O., Buchanan, C., Jepsen, R. D., Krause, K. P., Cashman, M. J., Gressler, B. P., Young, J. A., & Schmid, M. C. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116068 · Cited by 47
Rapinel, S., Panhelleux, L., Gayet, G., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13482 · Cited by 44
“The importance score of each explanatory variable in the RF model was calculated as the mean decrease in accuracy divided by its standard deviation across the 10 folds, and then scaled from 0 to 100 [95].”
MethodTsai, C., Lin, C. R., Kuo, H., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.7489 · Cited by 44
Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence
de Lacy, N., Ramshaw, M. J., McCauley, E., Kerr, K. F., Kaufman, J., & Kutz, J. N. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02599-9 · Cited by 47
“Extant studies using ML techniques to construct individual-level predictive classi fi ers of cases of youth mental illnesses typically compare the performance of multiple algorithm types across different classes to explore different ways of fi tting models to the data, a recommended practice [20].”
MethodJabeur, H., Drira, M., Rebaï, A., et al. (2017) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.7b00687 · Cited by 44
Peacock, W. F., Meter, T. E. V., Mirshahi, N., Ferber, K., Gerwien, R. W., Rao, V., Sair, H. I., Diaz-Arrastia, R. R., & Korley, F. K. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00641 · Cited by 47
Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA
Nolan, B. T., Green, C. T., Juckem, P. F., et al. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.02.029 · Cited by 44
Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions
Rátiva, D., Fernandes, B., & Roque, A. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2797983 · Cited by 43
“Test Set) is used to test the performance of the regression model [28].”
MethodA Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data
Egli, S., Thies, B., & Bendix, J. (2018) · Remote Sensing · doi:10.3390/rs10040628 · Cited by 44
Metamodeling for Groundwater Age Forecasting in the Lake Michigan Basin
Fienen, M. N., Nolan, B. T., Kauffman, L. J., et al. (2018) · Water Resources Research · doi:10.1029/2017wr022387 · Cited by 44
“Although complex models have low bias for training data, overfit models have high variance and poor forecast performance with new data, a condition referred to as the bias-variance trade-off (Kuhn & Johnson, 2013).”
BackgroundMiller, M. P., Carlisle, D. M., Wolock, D. M., et al. (2018) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.12685 · Cited by 44
Leveraging a luxury fashion brand through social media
Romão, M. T., Moro, S., Rita, P., & Ramos, P. (2018) · European Research on Management and Business Economics · doi:10.1016/j.iedeen.2018.10.002 · Cited by 44
“%) for testing it, thus assuring independence of the split between train and test data (Kuhn & Johnson, 2013).”
BackgroundSharon, H., Elamvazuthi, I., Lu, C., et al. (2019) · Sensors · doi:10.3390/s20010167 · Cited by 44
A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification
Courtenay, L. A., Huguet, R., González‐Aguilera, D., & Yravedra, J. (2019) · Applied Sciences · doi:10.3390/app10010150 · Cited by 44
Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images
Guan, Z., Abd-Elrahman, A. H., Fan, Z., et al. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.02.021 · Cited by 44
MOSQUERA-LOPEZ, C. M., Dodier, R. H., Tyler, N. S., Wilson, L. M., Youssef, J. E., Castle, J. R., & Jacobs, P. G. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0458 · Cited by 46
Leite, R. V., do Amaral, C. H., Pires, R. D. P., Silva, C. A., Soares, C. P. B., Macedo, R. P., da Silva, A. A. L., Broadbent, E. N., Mohan, M., & Leite, H. G. (2020) · Remote Sensing · doi:10.3390/rs12091513 · Cited by 44
“This method benefits from refitting fewer models at each step due to the variable rank creation [58].”
MethodMachine Learning Predictions of pH in the Glacial Aquifer System, Northern USA
Stackelberg, P. E., Belitz, K., Brown, C. J., Erickson, M. L., Elliott, S. M., Kauffman, L. J., Ransom, K. M., & Reddy, J. E. (2020) · Ground Water · doi:10.1111/gwat.13063 · Cited by 45
“A prediction is then generated as the average value of observations in each terminal node (Kuhn and Johnson 2016).”
MethodInfluentialTian, D., He, X., Srivastava, P., et al. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-021-02023-y · Cited by 44
“The M5-cubist model is a rule-based regression tree approach (Kuhn and Johnson 2013).”
MethodRen, Q., Ding, L., Dai, X., et al. (2021) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0003741 · Cited by 44
Huang, B., Dong, L., Zou, R., Yu, X., Guo, D., Huang, H., Liu, H., & Liu, Y. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-6624 · Cited by 44
“To investigate the contribution of different variables, an importance measure, the Gini importance, was used (26), which was computed as the total reduction of the criterion brought by that feature.”
MethodEvaluation of machine learning algorithms to classify and map landforms in Antarctica
Siqueira, R. G., Veloso, G. V., Filho, E. I. F., Francelino, M. R., Schaefer, C. E. G. R., & Corrêa, G. R. (2021) · Earth Surface Processes and Landforms · doi:10.1002/esp.5253 · Cited by 45
Putka, D. J., Oswald, F. L., Landers, R. N., Beatty, A. S., McCloy, R. A., & Yu, M. C. (2022) · Journal of Business and Psychology · doi:10.1007/s10869-022-09824-0 · Cited by 47
“We adopted a nested cross-validation framework for developing and cross-validating SPLS models (Cawley & Talbot, 2010; Kuhn & Johnson, 2013).”
MethodPrediction of severity of aviation landing accidents using support vector machine models
Silagyi, D. V., & Liu, D. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.107043 · Cited by 49
Wan, H., Qi, H., & Shang, S. (2023) · Agricultural Water Management · doi:10.1016/j.agwat.2023.108364 · Cited by 43
Toward human-level concept learning: Pattern benchmarking for AI algorithms
Holzinger, A., Saranti, A., Angerschmid, A., Finzel, B., Schmid, U., & Mueller, H. (2023) · Patterns · doi:10.1016/j.patter.2023.100788 · Cited by 54
A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression
Açıkkar, M., & Altunkol, Y. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08805-5 · Cited by 43
“On the contrary, hyperparameters are external to the model, and their values cannot be estimated from the given data, so they have to be set manually [1].”
BackgroundAbooali, D., & Soleimani, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-40466-1 · Cited by 43
Creating AI business value through BPM capabilities
Zebec, A., & Štemberger, M. I. (2024) · Business Process Management Journal · doi:10.1108/bpmj-07-2023-0566 · Cited by 60
Machine learning-assisted image-based optical devices for health monitoring and food safety
Mousavizadegan, M., Shalileh, F., Mostajabodavati, S., Mohammadi, J., & Hosseini, M. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117794 · Cited by 46
A systematic review of predictive modeling for bronchiolitis
Luo, G., Nkoy, F. L., Gesteland, P. H., Glasgow, T. S., & Stone, B. L. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.07.005 · Cited by 43
“[39] M.”
Wager, T. T., Kormos, B. L., Brady, J. T., Will, Y., Aleo, M. D., Stedman, D. B., Kühn, M., & Chandrasekaran, R. Y. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm401485p · Cited by 42
Self-citationChaves, L. F., Imanishi-Kobayashi, N., & Hoshi, T. (2015) · Bulletin of Entomological Research · doi:10.1017/s0007485315000474 · Cited by 43
“This model was simplified by a combined backward-elimination and forward-addition algorithm (Kuhn & Johnson, 2013) that minimized the Akaike Information Criterion (AIC).”
MethodMikoláš, M., Svitok, M., Bollmann, K., Reif, J., Bače, R., Janda, P., Trotsiuk, V., Čada, V., Vítková, L., Teodosiu, M., et al. (2017) · Forest Ecology and Management · doi:10.1016/j.foreco.2017.09.006 · Cited by 43
Ruis, A. R., Rosser, A. A., Quandt-Walle, C., Nathwani, J. N., Shaffer, D. W., & Pugh, C. M. (2017) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2017.11.027 · Cited by 42
Effect of olive trees density on the quality and composition of olive oil from cv. Arbequina
Rodrigues, N., Casal, S. I. P., Peres, A. M., et al. (2018) · Scientia Horticulturae · doi:10.1016/j.scienta.2018.04.059 · Cited by 43
“The statistical analysis was performed using the Subselect (Cadima et al., 2004; Cadima et al., 2012; Kuhn and Johnson, 2013) and MASS (Venables and Ripley, 2002) packages of the open source statistical program R (version 2.”
MethodInfluentialHawryło, P., & Wężyk, P. (2018) · Forests · doi:10.3390/f9050274 · Cited by 43
“The RF approach has a tuning parameter called mtry which is the number of randomly selected predictors, to choose from at each split [25,26].”
BackgroundHotel online reviews: creating a multi-source aggregated index
António, N., de Almeida, A., Nunes, L., Batista, F., & Ribeiro, R. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-05-2017-0302 · Cited by 42
Anderegg, J., Hund, A., Karisto, P., & Mikaberidze, A. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01355 · Cited by 41
“For classification, Partial Least Squares Discriminant Analysis (PLSDA) was used (for details on these methods we refer to Kuhn and Johnson, 2013 and citations therein).”
MethodLong-lead Prediction of ENSO Modoki Index using Machine Learning algorithms
Pal, M., Maity, R., Ratnam, J. V., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-57183-3 · Cited by 43
“The ML based prediction has generally shown good skill in forecasting events, though the method has limited capability to understand the underlying processes 19 .”
MethodRecognition of ECG signals using wavelet based on atomic functions
Hernandez-Matamoros, A., Fujita, H., Escamilla-Hernández, E., et al. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.02.007 · Cited by 43
Mowbray, F., Zargoush, M., Jones, A., de Wit, K., & Costa, A. P. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104163 · Cited by 43
Hu, L., Liu, B., Ji, J., & Li, Y. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016745 · Cited by 43
Aliev, K., & Antonelli, D. (2021) · Applied Sciences · doi:10.3390/app11041621 · Cited by 43
“Different feature selection such as multicollinearity, correlation coefficients and Variance Inflation Factors (VIF) are proposed by authors [39,40] to improve performance of the ML model outputs.”
BackgroundMethodEDLA-EFDS: A Novel Ensemble Deep Learning Approach For Electrical Fault Detection Systems
Elmasry, W., & Wadi, M. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107834 · Cited by 42
Metadata
- Authors
- Kuhn, M., & Johnson, K.
- Venue
- Springer eBooks
- Publisher
- Springer New York
- Published
- 2013
- OpenAlex
- W429766147
- Cited by
- 6,788
- References
- 0
- Citations by year
- 2026: 564 · 2025: 813 · 2024: 727 · 2023: 776 · 2022: 810 · 2021: 815 · 2020: 715 · 2019: 492 · 2018: 382 · 2017: 271 · 2016: 198 · 2015: 156 · 2014: 61 · 2013: 5
- FWCI
- 32.11
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- predictive modeling, data preprocessing, data partitioning, model calibration, model selection, model evaluation, classification, regression analysis
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