caret: Classification and Regression Training

By Max Kuhn. 2015

2,508 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/258c7e3242b91e02e092e77e058f6275ba52b12d

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Citations in the corpus

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning (2016, methodology): “Such standalone methods have been implemented mainly in R language (Torgo, 2010; Kuhn, 2015; Dal Pozzolo et al., 2013).”
Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean (2018, methodology): “These constituent models were fitted through the “caret” (Kuhn, 2016) package (Table S3).”
pdp: An R Package for Constructing Partial Dependence Plots (2017, unknown): “The partial function also supports objects of class "train" produced using the train function from the well-known caret package (Kuhn, 2017).”
50 Years of Data Science (2017, background): “Similarly, there are a flurry of tools for doing data science which are currently in vogue ( rmarkdown , the tidyverse, caret by Kuhn (2017), hadoop , Apache Spark ), some of which are built on fundamentally new approaches to data analysis and some that will be supplanted as technology changes.”
Finding Optimal Normalizing Transformations via bestNormalize (2021, background): “As a result, popular model selection packages such as caret (Kuhn, 2017) and recipes (Kuhn and Wickham, 2018) have built-in mechanisms to normalize the predictor variables (they call this “preprocessing”).”
Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, methodology): “In this paper, we investigate the performance of defect prediction models where Caret [30] — an off-the-shelf automated parameter optimization technique — has been applied.”
Handbook of Big Data (2016, methodology): “Support for other types of base learner and metalearner functions, such as those specified using the caret [5] R package, is on the development road map for the subsemble package.”
Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, background): “See Meyer et al. (2018) for a more detailed description on this FFS.”