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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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“This process is repeated multiple times to estimate the learning method’s generalization performance (Figure 6 II) (33,76).”
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“, 2013), separate models were created for their effect on each EF task, in order to produce more feasible and interpretable models (Kuhn and Johnson, 2013).”
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“Feature selection has several advantages, since it reduces overﬁtting and training time, as well as improves accuracy and has a long history in machine learning and in statistics [43–45].”
BackgroundMethod
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“The number of neighbours in the cubist model is applied to amend the rule-based prediction [39].”
Method
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
Ransom, K. M., Nolan, B. T., Traum, J. A., Faunt, C. C., Bell, A. M., Gronberg, J. A. M., Wheeler, D. C., Rosecrans, C. Z., Jurgens, B. C., Schwarz, G. E., et al. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.05.192 · Cited by 197
“Classification and regression tree (CART) methods subdivide the data with multiple nested binary splits, a procedure known as binary recursive partitioning (Hastie et al., 2009; Kuhn and Johnson, 2013).”
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“…the models we look at classi ﬁ cation accuracy based on cross-validation with re ﬁ tting implemented in the caret package via the setting ( Kuhn, 2008 ; Kuhn & Johnson, 2013 ): R > ctrl < -trainControl(method = "repeatedcv", number = 5, repeats = 2) which translates as: models are re ﬁ t ﬁ ve…”
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“In this algorithm, using multiple decision trees with the same distribution to set up a forest to train and predict the sample data (Kuhn and Johnson 2013).”
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“As a result, for the GBM model applied in R package, there are three main tuning parameters namely tree depth, number of trees and shrinkage (Kuhn and Johnson 2013).”
Method
Deep Reinforcement Learning
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“dust, mold, and pollen allergy collapsed into allergy [38].”
Background
A systematic review of personal thermal comfort models
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“The fractionated LUCAS site data were used to train and test the model, applying a repeated kfold cross-validation approach (Kuhn and Johnson, 2013) to identify best parameter values for the full variation of conditions at all 154 sites.”
Method
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“Instead, we used 10 repeated 3-fold cross- validation to provide reasonable estimates of the expected error (Kuhn and Johnson, 2013).”
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“15 Flexible discriminant analysis is a classification model based on a mixture of linear regression models, which uses optimal scoring to transform the response variable so that the data are in a better form for linear separation, and multiple adaptive regression splines to generate the…”
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“By adding the L1 penalty term to the Error term, the LASSO exploits the bias-variance trade- off to produce models that increase the bias in the model in order to greatly reduce the model variance and thereby combat the problem of collinearity (Kuhn, 2016).”
BackgroundMethodInfluential
Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.
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“a 3-fold cross validation was done in order to reduce the variance. This is based on the fact that using a smaller value of kfor cross validation often results in a smaller variance and a higher bias [49]. On the other hand, 5 dierent splits of data were used to reduce the bias of the models under consideration. The p…”
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“…models — the multiplelinear regression model, multiple stepwise regression model 75 , least angle regression model 76 , and elastic net model 77,78 — and four nonlinear models — the cubist model 79 , boosted tree model 75 , bagged tree model 80 , and random forest model 81 — were evaluated…”
INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data
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“90% non-SGA), the minority class (SGA or LGA) was upweighted prior to model development to achieve a 1:1 ratio of classes [11].”
Method
Big Data in Industrial-Organizational Psychology and Human Resource Management: Forward Progress for Organizational Research and Practice
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“Studies have shown that entering highly-correlated variables reduces the performance of models (Kuhn and Johnson 2013).”
Background
Nonparametric survival analysis using Bayesian Additive Regression Trees (BART)
Sparapani, R., Logan, B. R., McCulloch, R. E., & Laud, P. W. (2016) · Statistics in Medicine · doi:10.1002/sim.6893 · Cited by 160
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Social Determinants of Health and Race Disparities in Kidney Transplant
Wesselman, H. M., Ford, C. G., Leyva, Y. L., Li, X., Chang, C. H., Dew, M. A., Kendall, K., Croswell, E., Pleis, J. R., Ng, Y., et al. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.04860420 · Cited by 164
Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED
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“Toaddresstheclass imbalance in the critical care outcome (i.e., the low proportion of outcome), we chose the threshold of prospective prediction results based on ROC curve (i.e., the value with the shortest distance to the perfect model)[11].”
BackgroundMethodInfluential
Wind turbines cause functional habitat loss for migratory soaring birds
Marques, A. T., Santos, C. D., Hanssen, F., et al. (2019) · Journal of Animal Ecology · doi:10.1111/1365-2656.12961 · Cited by 161
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
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Personality Research and Assessment in the Era of Machine Learning
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Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP
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Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
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Global patterns of geo-ecological controls on the response of soil respiration to warming
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The min-cost parallel drone scheduling vehicle routing problem
Nguyen, M. A., Dang, G. T.-H., Hà, M. H., et al. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.07.008 · Cited by 157
“In our regression tree, the impurity of a node is measured by the Mean Squared Error (MSE ).”
Background
A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet Plateau with the effects of systematic anomalies removed
Ma, Z., Shi, Z., Zhou, Y., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.08.023 · Cited by 156
Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables
Cha, G.-W., Moon, H. J., & Kim, Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168530 · Cited by 158
“Preprocessing data are necessary to reduce the impact of data distortion or outliers and include cutting, adding, and transforming training datasets [43,44].”
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study
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Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns
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Interpretable classification models for recidivism prediction
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“0 is an updated version of the popular C4.5 algorithm (Quinlan 2014, Kuhn and Johnson 2013) that can create decision trees and rule sets.”
Background
A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA
Nolan, B. T., Fienen, M. N., & Lorenz, D. L. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.10.025 · Cited by 154
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis
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“…leave-n-out cross-validation) are known to provide less stable estimates than methods with random subsamp-ling (e.g. k-fold cross-validation) whereas the former is less-biased than the latter assuming all other factors are controlled i.e. this is a bias-variance trade-off (Kuhn & Johnson, 2013).”
Method
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“…2009) and represent, and (3) overfitting can be a problem, which makes it very important that trees are subjected to rigorous validation and pruning (see e.g. Kuhn and Johnson 2013: 182, Crawley 2013: Section 23.5, or James et al. 2013: 307 – 312), something that is often at least not reported on.”
BackgroundInfluential
A novel sub-epidemic modeling framework for short-term forecasting epidemic waves
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“Here y t i is the time series of incident cases describing the epidemic wave where t i are the time points of the time series data [31] .”
Background
Modelling heating and cooling energy demand for building stock using a hybrid approach
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A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
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Extreme learning machines for credit scoring: An empirical evaluation
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Radiomics in neuro-oncology: Basics, workflow, and applications
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“[20,21] ii) Wrapper methods (multivariate methods”
MRI of healthy brain aging: A review
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The use of machine learning for the identification of peripheral artery disease and future mortality risk
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Using Data Mining to Predict Hospital Admissions From the Emergency Department
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A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
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“Kuhn and Johnson [8] stated that “Unfortunately, the predictive models that are most powerful are”
Influential

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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