Applied Predictive Modeling

By Max Kuhn, Kjell Johnson. 2013

5,399 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “This is the main reason why a training-testing approach is used when dealing with ML problems [162, 163].”
Interpretable classification models for recidivism prediction (2015, background): “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.”
Finding Optimal Normalizing Transformations via bestNormalize (2021, background): “Another increasingly popular application of normalization occurs in applied regression settings with highly skewed distributions of the covariates (Kuhn and Johnson, 2013).”
A review of uncertainty analysis in building energy assessment (2018, background): “A meta package of Classification and Regression Training (caret) [203] is particularly useful because it combines a large number of machine learning methods to easily create several machine learning models and compare their predictive performance.”
Do we need hundreds of classifiers to solve real world classification problems? (2014, methodology): “Although it should be recommendable, we did not use the area under ROC curve as quality measure, nor develop cutoff tuning (Kuhn and Johnson, 2013), because some classifiers do not give probabilistic output.”
pre: An R Package for Fitting Prediction Rule Ensembles (2017, background): “0 is limited, but Kuhn and Johnson (2013) provide a rather complete description.”
Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (2016, methodology): “Predictive modeling is deﬁned as the process of developing a mathematical tool or model that generates an accurate prediction [24].”
Machine learning for predicting soil classes in three semi-arid landscapes (2015, methodology): “4 Covariate selection 289 290 Recursive feature elimination (Guyon et al., 2002; Kuhn and Johnson, 2013) was used to identify 291 an optimal subset of covariates from the set of all available covariates (covariate set 1+2) for each area 292 (Fig.”
Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods (2019, methodology): “Support vector machines (SVMs) Support vector machines (SVMs) are a family of regression models that are effective for nonlinear problems [20,21,24,25].”
A survey on wearable sensor modality centred human activity recognition in health care (2019, methodology): “The results of a k-fold cross-validation run are often summarized with the mean of the k times’ test (Kuhn & Johnson, 2013).”
Deep Reinforcement Learning: An Overview (2017, background): “2 MORE BOOKS • theoretical RL books (Bertsekas, 2012; Bertsekas and Tsitsiklis, 1996; Szepesvári, 2010) • an operations research oriented RL book (Powell, 2011) • an edited RL book (Wiering and van Otterlo, 2012) • Markov decision processes (Puterman, 2005) • deep learning (Deng and Dong, 2014) • ma”