Building Predictive Models in R Using the caret Package
Bibliographic Data
| ID | 23328706 |
|---|---|
| Authors | Max Kühn (0000-0003-2402-136X, corresponding author) |
| Year | 2008 |
| Volume | 28 |
| Issue | 5 |
| Publication date | 2008-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Statistical Software (JOURNAL) |
| Journal identifiers | ISSN: 1548-7660 • E-ISSN: 1548-7660 |
| Publisher | Foundation for Open Access Statistic (PUBLISHER) |
| DOI | 10.18637/jss.v028.i05 |
| OpenAlex | W1831050183 |
| Language | EN |
| Citations received | 288 |
| References cited | 2 |
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.
Benchmark (surveying) · Computational science · Data mining · Data set · Machine learning · Programming language · R package · Set (abstract data type) · Training set · Variable (mathematics) · Variety (cybernetics) · Artificial Intelligence · Computer Science · Data Analysis with R · Mathematics · Metabolomics and Mass Spectrometry Studies · Spectroscopy and Chemometric Analyses
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| Unique citing works | 288 |
|---|---|
| Citations per year | 13,71 |
| Citation span | 2005 - 2026 (22) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 275 |