Building Predictive Models in R Using the caret Package

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.

Keywords

Computer scienceBenchmark (surveying)R packageSet (abstract data type)Training setVariety (cybernetics)Variable (mathematics)Machine learningData setData miningArtificial intelligenceComputational scienceProgramming languageMathematics

Related Publications

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Alex Wang , Amanpreet Singh , Julian Michael +3 more

Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-o...

2018 3699 citations

MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

Jarrod D. Hadfield

Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...

2010 Journal of Statistical Software 4603 citations

Rethinking the Inception Architecture for Computer Vision

Christian Szegedy , Vincent Vanhoucke , Sergey Ioffe +2 more

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to bec...

2016 29577 citations

Improving neural networks by preventing co-adaptation of feature detectors

Geoffrey E. Hinton , Nitish Srivastava , Alex Krizhevsky +2 more

When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly...

2012 arXiv (Cornell University) 6630 citations

A general and simple method for obtaining R 2 from generalized linear mixed‐effects models

Shinichi Nakagawa , Holger Schielzeth

Summary The use of both linear and generalized linear mixed‐effects models ( LMM s and GLMM s) has become popular not only in social and medical sciences, but also in biological...

2012 Methods in Ecology and Evolution 9557 citations

Publication Info

Year: 2008
Type: article
Volume: 28
Issue: 5
Citations: 8313
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Building Predictive Models in R Using the caret Package

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

8313

OpenAlex

Cite This

APA Style

                            
                                    Max Kühn
                                
                            (2008). 
                            Building Predictive Models in <i>R</i> Using the <b>caret</b> Package. 
                            Journal of Statistical Software
                            , 28
                            (5)
                            .
                            https://doi.org/10.18637/jss.v028.i05

Identifiers

DOI: 10.18637/jss.v028.i05

Building Predictive Models in <i>R</i> Using the <b>caret</b> Package