{
  "id": "1312.2171",
  "version": "v3",
  "published": "2013-12-08T03:40:47.000Z",
  "updated": "2014-11-24T19:21:22.000Z",
  "title": "bartMachine: Machine Learning with Bayesian Additive Regression Trees",
  "authors": [
    "Adam Kapelner",
    "Justin Bleich"
  ],
  "comment": "39 pages, 13 figures, 4 tables, 2 appendices",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2014-02-28T18:38:50.000Z",
      "comment": "36 pages, 13 figures, 1 table, 1 algorithm, 2 appendices",
      "journal": null,
      "doi": null
    },
    {
      "version": "v3",
      "updated": "2014-11-24T19:21:22.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning",
      "bartmachine",
      "implementing bayesian additive regression trees",
      "large sample sizes",
      "model diagnostic plots"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 39,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1312.2171K"
    }
  }
}