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Open AccessJournal ArticleDOI

Building Predictive Models in R Using the caret Package

Max Kuhn
- 10 Nov 2008 - 
- Vol. 28, Iss: 5, pp 1-26
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TLDR
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R to simplify model training and tuning across a wide variety of modeling techniques.
Abstract
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.

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Citations
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Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas.

TL;DR: Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning.
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Clinical indicators of treatment-resistant psychosis.

TL;DR: People with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant and the genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.
Journal ArticleDOI

Compositional changes to the ileal microbiome precede the onset of spontaneous ileitis in SHIP deficient mice.

TL;DR: Findings indicate that SHIP is involved in maintaining ileal microbial homeostasis, and have broader implications for humans, since reduced SHIP protein levels have been reported in people with Crohn’s disease.
Journal ArticleDOI

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data.

TL;DR: An evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMs data is not appropriate.
References
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BookDOI

Modern Applied Statistics with S

TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.

Classification and Regression by randomForest

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.

Modern Applied Statistics With S

TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Proceedings ArticleDOI

Validity of the single processor approach to achieving large scale computing capabilities

TL;DR: In this paper, the authors argue that the organization of a single computer has reached its limits and that truly significant advances can be made only by interconnection of a multiplicity of computers in such a manner as to permit cooperative solution.