The caret Package

By Max Kuhn. 2007

756 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3f1f3e041c26b836ba511ce513afd75c84d3df8a

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Citations in the corpus

A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling (2019, methodology): “Many R packages provide random forest variable selection procedures, including boruta (Kursa & Rudnicki, 2010), varSelRF (Díaz-Uriarte & De Andres, 2006), VSURF (Genuer, Poggi, & Tuleau-Malot, 2015), caret (Kuhn, 2008), party (Hothorn, Hornik, Strobl, & Zeileis, 2010), randomForestSRC (Ishwaran & Ko”
A comparative study of machine learning classifiers for modeling travel mode choice (2017, methodology): “186 All modeling and analyses have been done in the R programming environment (R Core Team, 2015) using 187 the ’caret’ package (Kuhn, 2008).”
Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, methodology): “The neural network parameters were tuned within a leave-one-out cross validation of 200 iterations at a maximum with a number of units in the hidden layer between 1 and 10 and weight decay of 0, 0.01, 0.1 or 1 using the ‘train’ function of the ‘caret’ package in R [36].”
Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2020, unknown): “The Caret package from R is used for the implementation of the method ( Kuhn, 2008 ).”
Randomized Matrix Decompositions using R (2016, methodology): “First, we split the dataset into a training and a test set using the caret package (Kuhn 2008).”