{
  "id": "2204.13916",
  "version": "v1",
  "published": "2022-04-29T07:33:13.000Z",
  "updated": "2022-04-29T07:33:13.000Z",
  "title": "A study of tree-based methods and their combination",
  "authors": [
    "Yinuo Zeng"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, we describe a model combination strategy called the adaptive regression by mixing (ARM), which is feasible for tree- based methods via ISLE. Moreover, three modified ISLEs are proposed, and their performance are evaluated on the real data sets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-04-29T07:33:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "tree-based methods",
      "real data sets",
      "model combination strategy",
      "popular machine learning techniques",
      "general framework"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}