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

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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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“We adopt the 10-fold cross-validation approach (Kuhn & Johnson, 2013), in which an entire review data set is partitioned into 10.”
Method
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“Settings that are used to control the learning of the algorithm, but are not parameters tuned by the learning algorithm itself, are referred to as hyperparameters (Kuhn & Johnson, 2013).”
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“Boosting algorithms function by aggregating (boosting) several weak classifers (a weak classifer predicts marginally better than random) into an ensemble with improved accuracy [173].”
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“0 [16].”
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“This is done by creating a per-class mixture (Kuhn and Johnson, 2013), consisting in the separation of class-specific means from class-specific covariance structures.”
Method
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“This estimate does not replace and is not comparable with proper methods for predicting the outcome variable as used in statistical learning methods employing training and test datasets and different algorithms for prediction (e.g., Kuhn & Johnson, 2013).”
Method
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“Data Analysis We initially performed a graphical exploratory analysis evaluating the frequency, percentage, and near-zero variance for all categorical variables, distribution for numeric variables, and missing values and patterns of all variables (17).”
Method
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“The resulting class prediction for new instances is obtained by having each tree cast a class vote (the mode of all the votes is the resulting prediction of the random forest) (Kuhn and Johnson 2013).”
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“According to [35], we decided k is set as 5 because yield test error rate estimates that suffer neither from excessively high bias nor very high variance.”
Background
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“often achieve the highest prediction accuracy [8].”
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“This involves the following steps [64]:”
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Do the robot
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“There are several other optimizations, and the algorithm in its entirety is described in detail in Kuhn and Johnson (2013).”
Method
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“It is important to randomly reserve a portion of the whole data set to be included in the independent test set (Kuhn and Johnson, 2013).”
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“To avoid collinearity between potential explanatory variables, all explanatory variables with correlation coefficients >|0.75| were removed from further analyses as per Kuhn and Johnson (2013).”
MethodInfluential
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“This method usually results in lower variance and higher bias compared with repeated k-fold cross-validation (Kuhn & Johnson, 2013).”
Method
A Human(e) Factor in Clinical Decision Support Systems
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“Unfortunately, there is a trade-off between attainable model complexity and model interpretability [20].”
Background
Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data
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“The difference of this algorithm from the cross-validation partitioning method (Kuhn & Johnson, 2013) is that the drill holes are assigned to the folds in their entirety.”
Method
The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing
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Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete
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Importance of conserving large and old trees to continuity of tree‐related microhabitats
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“Fivefold cross-validated RMSE and BS values were calculated to ensure unbiased estimates of predictive performance (Kuhn & Johnson, 2013).”
Method
Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics
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Enhanced Selection of Assistance and Explosive Detection Dogs Using Cognitive Measures
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Global estimation of long-term persistence in annual river runoff
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Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach
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“The process of feature selection used here is primarily based on the strategy of adding the most informative predictors for the ORIENT-R model (Kuhn & Johnson, 2013) and assessing its performance.”
Method
Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence
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“CV is based on resampling procedure and very suitable for dealing with limited data samples, it results in less biased estimations and avoids the unnecessary optimism [59].”
Method
Coins for Bombs: The Predictive Ability of On‐Chain Transfers for Terrorist Attacks
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An intelligent DDoS attack detection tree-based model using Gini index feature selection method
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“, 2004), RandomForest (Liaw and Wiener, 2012) and caret (Kuhn and Johnson, 2013).”
Method
Predicting Arsenic in Drinking Water Wells of the Central Valley, California
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Projected trends in high-mortality heatwaves under different scenarios of climate, population, and adaptation in 82 US communities
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“We used Monte Carlo cross-validation to assess performance of these models (Kuhn and Johnson 2013) and selected four health-based classification models to use in the current study.”
Statistical Design of Experiments for Screening and Optimization
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Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
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Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks
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Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests
Guerra-Hernández, J., Narine, L. L., Pascual, A., et al. (2022) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2115599 · Cited by 78
“This function creates balanced splits in the data by ensuring that random sampling occurs within each forest type while also preserving the overall class distribution over the data set (Kuhn and Johnson 2013).”
Background
Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines
Zhou, J. P., Shi, X., HUANG, R.-D., et al. (2016) · Transactions of Nonferrous Metals Society of China · doi:10.1016/s1003-6326(16)64312-1 · Cited by 78
Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables
Iniesta, R., Hodgson, K., Stahl, D. R., Malki, K., Maier, W., Rietschel, M., Mors, O. N., Hauser, J., Henigsberg, N., Dernovšek, M. Z., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-23584-z · Cited by 78
Predictive Modelling of Training Loads and Injury in Australian Football
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“Principal component analysis (PCA) is a dimensionality reduction process that reduces a large number of predictor variables to a smaller number of uncorrelated variables [21] and has been employed in studies of training load monitoring [29].”
BackgroundMethodInfluential
Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression
Mason, C., Twomey, J. M., Wright, D., & Whitman, L. (2017) · Research in Higher Education · doi:10.1007/s11162-017-9473-z · Cited by 81
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“These imply that the modulated predictive model’s generalization was improved compared to the model derived by a single NBC (cf. Kuhn & Johnson, 2013), which is linked to generation of diverse pseudoreplicates from the original deposit and non-deposit cells that facilitate the model s…”
BackgroundMethodResultInfluential
Short-term renewable energy consumption and generation forecasting: A case study of Western Australia
Abu-Salih, B., Wongthongtham, P., Morrison, G. M., Coutinho, K., Al‐Okaily, M., & Huneiti, A. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09152 · Cited by 81
“This is through using data samples that were not used to build the model, the aim is to provide an unbiased evaluation of final model effectiveness [89], by means of evaluating the final hypothesis by an independent evaluation [90].”
Method
A Deep Transfer Learning Based Architecture for Brain Tumor Classification Using MR Images
Badjie, B., & Ülker, E. D. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.2.30835 · Cited by 76
Mapping forest biomass from space – Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models
Kattenborn, T., Maack, J., Fassnacht, F. E., et al. (2014) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2014.10.008 · Cited by 77
Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques
Coronel-Reyes, J., Ramírez-Morales, I., Fernández-Blanco, E., Rivero, D., & Pazos, A. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.12.030 · Cited by 77
“(Kuhn and Johnson, 2013, Refaeilzadeh et al., 2009).”
Method
Dashing hopes? The predictive accuracy of domestic abuse risk assessment by police
Turner, E., Medina, J., & Brown, G. (2019) · The British Journal of Criminology · doi:10.1093/bjc/azy074 · Cited by 78
Assessing spatial predictive models in the environmental sciences: Accuracy measures, data variation and variance explained
Li, J. (2016) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2016.02.004 · Cited by 77
“With the advancement in computing technology and modelling techniques (Crawley, 2007; Hastie et al., 2009; Kuhn and Johnson, 2013) and development in remote sensing and geographic information systems, predictive models have been increasingly employed for making spatially continuous predictions in…”
Method
A hybrid ensemble method for improved prediction of slope stability
Qi, C., & Tang, X. (2018) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.2834 · Cited by 76
Post-cutting Mortality Following Experimental Silvicultural Treatments in Unmanaged Boreal Forest Stands
Girona, M. M., Morin, H., Lussier, J.-M., & Ruel, J. (2019) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2019.00004 · Cited by 72
Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances
Schultebraucks, K., & Galatzer‐Levy, I. R. (2019) · Journal of Traumatic Stress · doi:10.1002/jts.22384 · Cited by 78
Comparing predictions of fisheries bycatch using multiple spatiotemporal species distribution model frameworks
Stock, B. C., Ward, E. J., Eguchi, T., Jannot, J. E., Thorson, J. T., Feist, B. E., & Semmens, B. X. (2019) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2018-0281 · Cited by 74
Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran
Shafizadeh‐Moghadam, H., Weng, Q., Liu, H., & Valavi, R. (2020) · GIScience & Remote Sensing · doi:10.1080/15481603.2020.1736857 · Cited by 76
“…NDBI and NDVI which can potentially take values between −1 and 1 are calculated as follows; Lλ ¼ gain DNþ offset (1) NDBI ¼ b6 b5 b6þ b5 (2) NDVI ¼ b5 b4 b5þ b4 (3) Collinearity and multicollinearity can add more complexity to themodel and influence themodel’s performance (Kuhn and Johnson 2013).”
Background
Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback
Sankar, S., Balamurugan, D., Brown, A. J. V., Ding, K., Xu, X., Low, J.-H., Yeow, C., & Thakor, N. V. (2020) · Soft Robotics · doi:10.1089/soro.2020.0016 · Cited by 78
“55,56 This procedure randomly splits the data set into k groups.”
A critical review of decision support systems for brownfield redevelopment
Hammond, E. B., Coulon, F., Hallett, S. H., Thomas, R., Hardy, D., Kingdon, A., & Beriro, D. J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.147132 · Cited by 74
“Some example applications include image classification, forecasting stock prices, simulating recovery rates in patients, and modelling fluid dynamic systems (Kuhn and Johnson, 2013).”
BackgroundInfluential
Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters
Bourel, M., Segura, A. M., Crisci, C., López, G., Sampognaro, L., Vidal, V., Kruk, C., Piccini, C., & Perera, G. (2021) · Water Research · doi:10.1016/j.watres.2021.117450 · Cited by 75
Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., & Nussbaum, M. (2021) · Geoderma Regional · doi:10.1016/j.geodrs.2021.e00437 · Cited by 83
Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review
Mao, Y.-J., Lim, H.-J., Ni, M., Yan, W.-H., Wong, D. W., & Cheung, J. C. (2022) · Cancers · doi:10.3390/cancers14020367 · Cited by 80
“In the case of cross-validation (a bootstrap approach), the training, validation and testing datasets are nested without data splitting [91] and have been recommended for small sample sizes (e.”
Method
Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction
Tran, V. Q. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131683 · Cited by 77
Artificial Intelligence in Periodontology: A Scoping Review
Scott, J., Biancardi, A., Jones, O. P. H., & Andrew, D. (2023) · Dentistry Journal · doi:10.3390/dj11020043 · Cited by 88
Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
Tseng, C.-J., Lu, C.-J., Chang, C.-C., Chen, G.-D., & Cheewakriangkrai, C. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.06.003 · Cited by 76

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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