Cited by 6,788Statistical Methods and ApplicationsData Mining and Machine Learning ApplicationsStatistical Methods and Inference

The DOI matches its Crossref record.

Applied Predictive Modeling

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

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

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-1-4614-6849-3

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 501–600 of 1,024

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data
Wallert, J., Tomasoni, M., Madison, G., & Held, C. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0500-y · Cited by 76
How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset
Tyralis, H., Papacharalampous, G., & Tantanee, S. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.070 · Cited by 75
“To test the predictive performance of the regression models (naïve, linear or random forests) 10-fold cross validation is performed (Kuhn and Johnson 2013, pp. 69–71).”
Method
Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context
Straka, M., Falco, P. D., Ferruzzi, G., Proto, D., van der Poel, G., Khormali, S., & Buzna, Ľ. (2020) · IEEE Access · doi:10.1109/access.2020.2965621 · Cited by 75
Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms
Vakhshoori, V., Pourghasemi, H. R., Zare, M. A., et al. (2019) · Water · doi:10.3390/w11112292 · Cited by 75
Depression screening using mobile phone usage metadata: a machine learning approach
Razavi, R., Gharipour, A., & Gharipour, M. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz221 · Cited by 74
“To ensure that the performance of the models was assessed on a sufficient number of samples and that the reported prediction performance was not specific to a single test subset, the above process was repeated 10 times, each time with new random training and test partitions and performance results were subsequently av…”
BackgroundMethodInfluential
Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study
van Mens, K., de Schepper, C., Wijnen, B. F. M., Koldijk, S., Schnack, H. G., de Looff, P. C., Lokkerbol, J., Wetherall, K., Cleare, S., O'Connor, D. B., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.03.081 · Cited by 73
“In cross-validation, the data are divided repeatedly into several subsets and algorithms are trained on a subset called the training set, and are evaluated on a different subset called the test set (Kuhn and Johnson, 2013).”
Method
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
Yang, Y., & Chui, T. F. M. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-5839-2021 · Cited by 81
Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence
Vulova, S., Meier, F., Rocha, A. D., Quanz, J., Nouri, H., & Kleinschmit, B. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.147293 · Cited by 75
An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models
Wänke, P., Barros, C. P., & Chen, Z. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2015.07.028 · Cited by 75
“In a broader sense, a predictive analysis aims to obtain forecasting, as stated in Faraway (2006) and in Kuhn and Johnson (2013).”
Background
Beyond Early Warning Indicators: High School Dropout and Machine Learning
Sansone, D. (2018) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.12277 · Cited by 86
The influence of weather and weather variability on mosquito abundance and infection with West Nile virus in Harris County, Texas, USA
Poh, K. C., Chaves, L. F., Reyna, M., Roberts, C. M., Fredregill, C., Bueno, R., Debboun, M., & Hamer, G. L. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.04.109 · Cited by 76
“Models were simplified by model selection through backward elimination (Kuhn and Johnson, 2013), following the minimization of the Akaike Information Criterion (AIC) (Faraway, 2016).”
Method
A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators
Peng, J., Chen, C., Ζhou, M., Xie, X., Zhou, Y., & Luo, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-60042-1 · Cited by 76
“0 decision tree is a classification approach which generates the tree in top-down scheme based on the information using a recursive process 26,27 .”
Method
Predictive modelling of training loads and injury in Australian football
Carey, D. L., Ong, K.-L., Whiteley, R. J., Crossley, K. M., Crow, J. F., & Morris, M. E. (2018) · Figshare · doi:10.26181/22769228.v1 · Cited by 75
Bayesian belief network models to analyse and predict ecological water quality in rivers
Forio, M. A. E., Landuyt, D., Bennetsen, E., et al. (2015) · Ecological Modelling · doi:10.1016/j.ecolmodel.2015.05.025 · Cited by 75
A few simple steps to improve the description of group results in neuroscience
Rousselet, G. A., Foxe, J. J., & Bolam, J. P. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13400 · Cited by 75
Convergence of Gamification and Machine Learning: A Systematic Literature Review
Khakpour, A., & Colomo‐Palacios, R. (2020) · Technology Knowledge and Learning · doi:10.1007/s10758-020-09456-4 · Cited by 73
“Kuhn and Johnson (2013) believe that machine learning can be interchangeably called as predictive modeling which in turn can also be described as the process of developing a mathematical tool or model that generates an accurate prediction.”
Background
Dynamic ambulance dispatching: is the closest-idle policy always optimal?
Jagtenberg, C. J., Bhulai, S., & van der Mei, R. D. (2016) · Health Care Management Science · doi:10.1007/s10729-016-9368-0 · Cited by 74
“We used random forests to create the decision tree, since it is known that a basic CART has poor predictive performance (see Chapter 14 of (15)).”
Method
Distinguishing butchery cut marks from crocodile bite marks through machine learning methods
Domínguez‐Rodrigo, M., & Baquedano, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-24071-1 · Cited by 74
Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach
Wänke, P., Azad, M. A. K., Barros, C. P., et al. (2015) · Expert Systems · doi:10.1111/exsy.12144 · Cited by 73
“These parameters are usually referred to as tuning parameters as there is no analytical formula to determine appropriate values for them (Kuhn & Johnson, 2013).”
Background
Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm
Godinho, S., Guiomar, N., & Gil, A. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1399480 · Cited by 72
Statistical modeling methods: challenges and strategies
Henley, S. S., Golden, R. M., & Kashner, T. M. (2019) · Biostatistics & Epidemiology · doi:10.1080/24709360.2019.1618653 · Cited by 70
Cover crops and weed suppression in the U.S. Midwest: A meta-analysis and modeling study
Nichols, V., Martinez‐Feria, R. A., Weisberger, D. A., Carlson, S., Basso, B., & Basche, A. (2020) · Lincoln (University of Nebraska) · Cited by 74
Crosstalk between codon optimality and cis-regulatory elements dictates mRNA stability
Medina-Muñoz, S. G., Kushawah, G., Castellano, L. A., Diez, M., DeVore, M. L., Salazar, M. J. B., & Bazzini, A. (2021) · Genome biology · doi:10.1186/s13059-020-02251-5 · Cited by 69
“S4a) was derived using a supervised dimensional reduction technique, partial least squares (PLS) [61], that finds components that maximally summarize the variation of the codon content while simultaneously requiring these components to have a maximum correlation with the mRNA stability profiles [62].”
Method
Modeling and Sensitivity Analysis of Concrete Creep with Machine Learning Methods
Li, K., Long, Y., Wang, H., & Wang, Y. (2021) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0003843 · Cited by 77
Machine Learning Algorithms Predict Achievement of Clinically Significant Outcomes After Orthopaedic Surgery: A Systematic Review
Kunze, K. N., Krivicich, L. M., Clapp, I. M., Bodendorfer, B. M., Nwachukwu, B. U., Chahla, J. A., & Nho, S. J. (2021) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2021.12.030 · Cited by 77
Prediction of learning outcomes with a machine learning algorithm based on online learning behavior data in blended courses
Luo, Y., Han, X., & Zhang, C. (2022) · Asia Pacific Education Review · doi:10.1007/s12564-022-09749-6 · Cited by 86
“As this difference decreases, the bias of the technique falls (Kuhn & Johnson, 2013).”
Automatic cross-validation in structured models: Is it time to leave out leave-one-out?
Adín, A., Krainski, E. T., Lenzi, A., Liu, Z., Martínez‐Minaya, J., & Rue, H. (2024) · Spatial Statistics · doi:10.1016/j.spasta.2024.100843 · Cited by 85
Cutaneous Leishmaniasis and Sand Fly Fluctuations Are Associated with El Niño in Panamá
Chaves, L. F., Calzada, J. E., Valderrama, A., & Saldaña, A. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0003210 · Cited by 74
“…understand the process of model association we studied the correlation of the variables considered in the full model by estimating the correlation between all variables pairs and plotting the correlations in color and size categories according to their and magnitude, building a corrgram [55].”
Herbivore‐induced plant volatiles accurately predict history of coexistence, diet breadth, and feeding mode of herbivores
Danner, H., Desurmont, G. A., Cristescu, S. M., et al. (2017) · New Phytologist · doi:10.1111/nph.14428 · Cited by 73
An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles
Li, X., Shu, X., Shen, J., Xiao, R., Yan, W., & Chen, Z. (2017) · Energies · doi:10.3390/en10050691 · Cited by 72
“Currently, there are three types of cross-validation methods: leave group out cross-validation (LGOCV), K-fold cross-validation (K-fold CV), and leave one out cross-validation with the repeating K-fold cross-validation method [28,40].”
Method
Pregabalin for the Prevention of Oxaliplatin-Induced Painful Neuropathy: A Randomized, Double-Blind Trial
de Andrade, D. C., Teixeira, M. J., Galhardoni, R., Ferreira, K. S. L., Malieno, P. B., Scisci, N., Zandonai, A. P., Teixeira, W. G. J., Saragiotto, D. F., da Silva, V. A., et al. (2017) · The Oncologist · doi:10.1634/theoncologist.2017-0235 · Cited by 73
“Categorical variables were evaluated for near-zero variation [38].”
Background
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusionPreprint
Phillips, T. R. F., Heaney, C. E., Smith, P. N., & Pain, C. C. (2020) · arXiv (Cornell University) · doi:10.1002/nme.6681 · Cited by 68
Machine learning enhances prediction of illness course: a longitudinal study in eating disorders
Haynos, A. F., Wang, S. B., Lipson, S., Peterson, C. B., Mitchell, J. E., Halmi, K. A., Agras, W. S., & Crow, S. J. (2021) · Psychological Medicine · doi:10.1017/s0033291720000227 · Cited by 73
“…although cross-validation is an accepted ML practice for investigations with smaller sample sizes and we conducted upsampling within the training (but not test) datasets (Kuhn & Johnson, 2013), it has the potential to yield overly optimistic models (Smith, Seaman, Wood, Royston, & White, 2014).”
Method
Spatial characteristics of professional tennis serves with implications for serving aces: A machine learning approach
Whiteside, D., & Reid, M. M. (2016) · Journal of Sports Sciences · doi:10.1080/02640414.2016.1183805 · Cited by 72
“Kuhn and Johnson (2013) report that predictive accuracy is generally maximised when one does not desire to understand the underlying features of the model (which is true in many real-world applications, e.g., successfully detecting credit card fraud).”
Background
Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning
Alsaleh, R., & Sayed, T. (2020) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2020.02.007 · Cited by 72
Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study
Balogun, A. O., Basri, S. B., Mahamad, S., Abdulkadir, S. J., Almomani, M. A., Adeyemo, V. E., Al-Tashi, Q., Mojeed, H. A., Imam, A. A., & Bajeh, A. O. (2020) · Symmetry · doi:10.3390/sym12071147 · Cited by 82
“10-fold CV has been proven empirically to generate test error rates with low bias and low variance [50,51].”
Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study
Sudarshan, V. K., Brabrand, M., Range, T. M., & Wiil, U. K. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104541 · Cited by 77
Artificial Neural Network Hyperparameters Optimization: A Survey
Kadhim, Z. S., Abdullah, H. S., & Ghathwan, K. I. (2022) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v18i15.34399 · Cited by 79
Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment
Ejaz, U., Khan, S. M., Jehangir, S., et al. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141877 · Cited by 72
Predictive Method for Correct Identification of Archaeological Charred Grape Seeds: Support for Advances in Knowledge of Grape Domestication Process
Ucchesu, M., Orrù, M., Grillo, O., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149814 · Cited by 72
Non-invasive differentiation between fresh and frozen/thawed tuna fillets using near infrared spectroscopy (Vis-NIRS)
dos Reis, M. M., Martínez, E., Saitua, E., et al. (2016) · LWT · doi:10.1016/j.lwt.2016.12.014 · Cited by 72
Reading Emotion From Mouse Cursor Motions: Affective Computing Approach
Yamauchi, T., & Xiao, K. (2017) · Cognitive Science · doi:10.1111/cogs.12557 · Cited by 78
Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability
Qi, C., Fourie, A. B., Ma, G., Tang, X., & Du, X. (2017) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000737 · Cited by 73
A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study
Sharafoddini, A., Dubin, J. A., Maslove, D. M., & Lee, J. (2019) · JMIR Medical Informatics · doi:10.2196/11605 · Cited by 77
Synthetic resampling strategies and machine learning for digital soil mapping in Iran
Taghizadeh‐Mehrjardi, R., Schmidt, K., Eftekhari, K., Behrens, T., Jamshidi, M., Davatgar, N., Toomanian, N., & Scholten, T. (2019) · European Journal of Soil Science · doi:10.1111/ejss.12893 · Cited by 72
“The three well-known tuning parameters of RF models are mtry (the number of predictor variables), ntree (the number of trees) and sampsize (the size of sample to be used in each tree), and they are optimized by caret (Behrens, Zhu, Schmidt, & Scholten, 2010; Kuhn & Johnson, 2013).”
MethodInfluential
Influence of Severity of Type and Timing of Retrospectively Reported Childhood Maltreatment on Female Amygdala and Hippocampal Volume
Herzog, J., Thome, J., Demirakça, T., Koppe, G., Ende, G., Lis, S., Rausch, S., Priebe, K., Müller‐Engelmann, M., Steil, R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-57490-0 · Cited by 85
Coupling Terrestrial Laser Scanning with 3D Fuel Biomass Sampling for Advancing Wildland Fuels Characterization
Rowell, E. M., Loudermilk, E. L., Hawley, C. S., Pokswinski, S., Seielstad, C., Queen, L. P., O’Brien, J. J., Hudak, A. T., Goodrick, S. L., & Hiers, J. K. (2019) · Forest Ecology and Management · doi:10.1016/j.foreco.2020.117945 · Cited by 77
Development of a Geogenic Radon Hazard Index—Concept, History, Experiences
Bossew, P., Cinelli, G., Ciotoli, G., Crowley, Q., Cort, M. D., Elío, J., Gruber, V., Petermann, E., & Tollefsen, T. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17114134 · Cited by 73
“The algorithm Multivariate Adaptive Regression Splines (MARS) (an introduction can be found in [110] and [114]) creates piecewise linear models where each predictor models an isolated part of the original data.”
Background
Marine Sedimentary Carbon Stocks of the United Kingdom’s Exclusive Economic Zone
Smeaton, C., Hunt, C. A., Turrell, W. R., et al. (2021) · Frontiers in Earth Science · doi:10.3389/feart.2021.593324 · Cited by 71
Characterization and prediction of chemical functions and weight fractions in consumer products
Isaacs, K., Goldsmith, M., Egeghy, P. P., Phillips, K. A., Brooks, R. D., Hong, T., & Wambaugh, J. F. (2016) · Toxicology Reports · doi:10.1016/j.toxrep.2016.08.011 · Cited by 71
Application of Cloud Model in Rock Burst Prediction and Performance Comparison with Three Machine Learning Algorithms
Lin, Y., Zhou, K., & Li, J. (2018) · IEEE Access · doi:10.1109/access.2018.2839754 · Cited by 71
“The tenfold cross validation (10-fold CV) method [37] is commonly used to test the accuracy of algorithms.”
Method
Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach
Aziz, F., Malek, S., Ibrahim, K. S., Shariff, R. E. R., Ahmad, W. A. W., Ali, R. M., Liu, K. T., Selvaraj, G., & Kasim, S. S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254894 · Cited by 71
“Stratified random sampling of data was used [12].”
Method
Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations
Duckworth, C., Guy, M., Kumaran, A., O’Kane, A. A., Ayobi, A., Chapman, A. P., Marshall, P., & Boniface, M. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221103561 · Cited by 76
BERT- and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: A comparative study
Gomes, L. A. F., Torres, R. D. S., & Côrtes, M. L. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107217 · Cited by 85
Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels
Ma, M., Lu, J., & Tryggvason, G. (2016) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2016.06.021 · Cited by 69
Predicting Residential Water Demand with Machine-Based Statistical Learning
Lee, D., & Derrible, S. (2019) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001119 · Cited by 73
Data Science for Weather Impacts on Crop Yield
Konduri, V. S., Vandal, T., Ganguly, S., & Ganguly, A. R. (2020) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2020.00052 · Cited by 75
Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation
Ismail, L., Materwala, H., Tayefi, M., Ngo, P., & Karduck, A. P. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09582-x · Cited by 75
“It shows that the number of observations in the positive class (negative class) for the UCI (MIMIC III) dataset is very high compared to that in the negative (positive) class, leading to imbalanced datasets [74].”
Background
Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance
Shafizadeh‐Moghadam, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115678 · Cited by 69
Predictive maintenance of pumps in civil infrastructure: State-of-the-art, challenges and future directions
Hallaji, S. M., Fang, Y., & Winfrey, B. K. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104049 · Cited by 77
Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry
Domínguez-Monferrer, C., Fernández-Pérez, J., Santos, R. D. A., et al. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.10.018 · Cited by 69
Multiclass Paddy Disease Detection Using Filter-Based Feature Transformation Technique
Bharanidharan, N., Chakravarthy, S. R. S., Rajaguru, H., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3322587 · Cited by 69
“Even though there are hundreds of ML algorithms, none of them can be stated as the best for all type of applications since these ML algorithms are data-dependent [13].”
Background
A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data
M’hamdi, O., Takács, S., Palotás, G., Ilahy, R., Helyes, L., & Pék, Z. (2024) · Plants · doi:10.3390/plants13050746 · Cited by 72
Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
Luo, M., Anees, S. A., Huang, Q., Xin, Q., Qin, Z., Fan, J., Han, G., Zhang, L., & Shafri, H. Z. M. (2024) · Forests · doi:10.3390/f15060975 · Cited by 71
“Details on the spatial cross-validation of forest AGB estimation with machine learning algorithms could be found in Kuhn and Johnson [38].”
Method
Ozone effects on European forest growth—Towards an integrative approach
Cailleret, M., Ferretti, M. P., Geßler, A., Rigling, A., & SCHAUB, M. C. (2018) · Journal of Ecology · doi:10.1111/1365-2745.12941 · Cited by 75
“…uncertainty about model structure and parameterization, we recommend the use and joint interpretation of multiple correlative modelling approaches, from multivariable fixed or mixed effects models to Structural Equation Models, including regressions by RandomForest (see Kuhn & Johnson, 2013).”
Method
Characterization of coronary artery pathological formations from OCT imaging using deep learning
Abdolmanafi, A., Duong, L., Dahdah, N., Ibrahim, R., & Farida Cheriet, A. (2018) · Biomedical Optics Express · doi:10.1364/boe.9.004936 · Cited by 69
An improved model for gas-liquid flow pattern prediction based on machine learning
Mask, G. M., Wu, X., & Ling, K. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106370 · Cited by 69
Adoption Factors of FinTech: Evidence from an Emerging Economy Country-Wide Representative Sample
Mahmud, K., Joarder, M. M. A., & Muheymin-Us-Sakib, K. (2022) · International Journal of Financial Studies · doi:10.3390/ijfs11010009 · Cited by 68
“Each successive step consists of elimination of lower ranking variable(s) and re-estimation of the model with remaining features (Kuhn and Johnson 2013).”
BackgroundMethod
Predictive Modeling for Metabolomics Data
Ghosh, T., Zhang, W., Ghosh, D., & Kechris, K. J. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0239-3_16 · Cited by 74
Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases
Obasi, T., & Shafiq, M. O. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005488 · Cited by 69
“We configured the k value to be ten because it has been found (through experimentation) to normally result in a model skill estimate with lower bias a modest variance [39].”
Method
Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach
Yang, J., Jiang, L., Lemmetyinen, J., Pan, J., Luojus, K., & Takala, M. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112630 · Cited by 70
A history and evaluation of catch‐only stock assessment models
Ovando, D., Free, C. M., Jensen, O. P., & Hilborn, R. W. (2021) · Fish and Fisheries · doi:10.1111/faf.12637 · Cited by 74
“Best practices in predictive modelling would have the model tested on a set of data fundamentally isolated from its development (Kuhn & Johnson, 2013).”
Method
Enhancing customer retention in telecom industry with machine learning driven churn prediction
Sikri, A., Jameel, R., Idrees, S. M., & Kaur, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-63750-0 · Cited by 69
Predicting Redox Conditions in Groundwater at a Regional Scale
Tesoriero, A. J., Terziotti, S., & Abrams, D. B. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b01869 · Cited by 68
Formal Models of the Network Co-occurrence Underlying Mental Operations
Bzdok, D., Varoquaux, G. P., Grisel, O., Eickenberg, M., Poupon, C., & Thirion, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004994 · Cited by 61
“We then inspected the learned classification models based on task-derived network dictionaries as to whether they were fit for purpose [52].”
Method
Window of implantation transcriptomic stratification reveals different endometrial subsignatures associated with live birth and biochemical pregnancy
Díaz-Gimeno, P., Ruíz-Alonso, M., Sebastián-León, P., Pellicer, A., Valbuena, D., & Simón, C. (2017) · Fertility and Sterility · doi:10.1016/j.fertnstert.2017.07.007 · Cited by 70
“The caret statistical R package (20, 21) was implemented for prediction performance.”
Method
Predicting soil texture using image analysis
Morais, P. A. D. O., de Souza, D. M., Carvalho, M. T. D. M., et al. (2019) · Microchemical Journal · doi:10.1016/j.microc.2019.01.009 · Cited by 68
Soil Management Effects on Soil Water Erosion and Runoff in Central Syria—A Comparative Evaluation of General Linear Model and Random Forest Regression
Mohammed, S., Al-Ebraheem, A., Holb, I. J., et al. (2020) · Water · doi:10.3390/w12092529 · Cited by 68
“In this case, R2 was not the traditional one referring to the response and predictor variables, but, as a pseudo-R2, it was the square of the correlation between the observed and modelled values [63].”
Background
Selecting Machine Learning Algorithms Using Regression Models
Doan, T., & Kalita, J. K. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.43 · Cited by 75
Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms
Zhu, C., Tian, W., Yin, B., et al. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115025 · Cited by 67
Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
Pintelas, E. G., Liaskos, M., Livieris, I. E., Kotsiantis, S. B., & Pintelas, P. (2020) · Journal of Imaging · doi:10.3390/jimaging6060037 · Cited by 66
“In recent days, interpretability/explainability in machine learning domain has become a significant issue, since much of real-world problems require reasoning and explanation on predictions, while it is also essential to understand the model’s prediction mechanism in order to trust it and make decisions on critical is…”
Background
Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium
Munck, N. S. M., Njage, P. M. K., Leekitcharoenphon, P., Litrup, E., & Hald, T. (2020) · Risk Analysis · doi:10.1111/risa.13510 · Cited by 70
“The held-out-subset was returned to the training set and the procedure was repeated until all subsets had been held out and predicted (Kuhn & Johnson, 2013).”
BackgroundMethodInfluential
Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data
Ahmadi, K., Kalantar, B., Saeidi, V., et al. (2020) · Remote Sensing · doi:10.3390/rs12183019 · Cited by 67
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran
Shafizadeh‐Moghadam, H., Minaei, M., Pontius, R. G., et al. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101595 · Cited by 67
Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
Jauhiainen, S., Kauppi, J., Krosshaug, T., Bahr, R., Bartsch, J., & Äyrämö, S. (2022) · The American Journal of Sports Medicine · doi:10.1177/03635465221112095 · Cited by 68
“On the other hand, this high learning capacity risks overfitting, and therefore it is critical to test the generalizability of a predictive model properly before it is implemented into practice.(22) Importantly, the role of chance results should be considered, ensuring that the predictive performance is better than ch…”
BackgroundMethod
Mapping green innovation with machine learning: Evidence from China
Liu, F., Wang, R., & Fang, M. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123107 · Cited by 69
Comparative Study on Machine Learning for Urban Building Energy Analysis
Wei, L., Tian, W., Silva, E. A., et al. (2015) · Procedia Engineering · doi:10.1016/j.proeng.2015.08.1070 · Cited by 67
Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes
Ghosh, A., & Joshi, P. K. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.07.003 · Cited by 69
Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset
Czernecki, B., Nowosad, J., & Jabłońska, K. (2018) · International Journal of Biometeorology · doi:10.1007/s00484-018-1534-2 · Cited by 67
“The overall performance was obtained by averaging the k estimates of the performance (Kuhn and Johnson 2013).”
The impact of global warming on lake surface water temperature in Poland - the application of empirical-statistical downscaling, 1971-2100
Czernecki, B., & Ptak, M. (2018) · Journal of Limnology · doi:10.4081/jlimnol.2018.1707 · Cited by 67
Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem
Große‐Stoltenberg, A., Hellmann, C., Werner, C., Oldeland, J., & Thiele, J. (2016) · Remote Sensing · doi:10.3390/rs8040334 · Cited by 72
“longifolia” was up-sampled during training (Table 2) to avoid a high impact of the majority class on the classifier which could lead to poor identification of the minority class [68].”
BackgroundMethod
Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants
Worrell, C. L., Luangkesorn, L., Haight, J. M., et al. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.11.014 · Cited by 66
Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning
Miché, M., Studerus, E., Meyer, A. H., Gloster, A. T., Beesdo‐Baum, K., Wïttchen, H., & Lieb, R. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.093 · Cited by 69
On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification
Pawłuszek-Filipiak, K., & Borkowski, A. (2020) · Remote Sensing · doi:10.3390/rs12183054 · Cited by 68
“For this reason, we adopted the predominant terminology used in the machine learning (ML) community [38,39].”
Method
Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms
Elkhrachy, I. (2022) · Remote Sensing · doi:10.3390/rs14030440 · Cited by 71
“Any ML predictive modeling project is unique, but there are common basic processes, such as identifying the problem, preparing the data, and assessing and finalizing models [25].”
Background
Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
Singh, S., Patro, S. K., & Parhi, S. K. (2023) · Asian Journal of Civil Engineering · doi:10.1007/s42107-023-00698-y · Cited by 69
Modeling Air Pollution, Climate, and Health Data Using Bayesian Networks: A Case Study of the English Regions
Vitolo, C., Scutari, M., Ghalaieny, M., et al. (2018) · Earth and Space Science · doi:10.1002/2017ea000326 · Cited by 65
“Since such variables are noninformative under the distributional assumptions used for structure learning below (Kuhn & Johnson, 2013), we disregard them (region, zone, type, and year) when running the EM algorithm.”
Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus
VanderWaal, K. L., Perez, A. M., Torremorrell, M., Morrison, R., & Craft, M. E. (2018) · Epidemics · doi:10.1016/j.epidem.2018.04.001 · Cited by 68
“%) datasets, as recommended by (Kuhn and Johnson, 2013).”
Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
Karhade, A. V., Shah, A. A., Bono, C. M., Ferrone, M. L., Nelson, S. B., Schoenfeld, A. J., Harris, M. B., & Schwab, J. H. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.06.024 · Cited by 67
Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques
Qi, C., Chen, Q., Dong, X., et al. (2019) · Powder Technology · doi:10.1016/j.powtec.2019.11.046 · Cited by 66

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Applied Predictive Modeling

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks