{
  "id": "2007.07588",
  "version": "v1",
  "published": "2020-07-15T10:06:59.000Z",
  "updated": "2020-07-15T10:06:59.000Z",
  "title": "Importance of Tuning Hyperparameters of Machine Learning Algorithms",
  "authors": [
    "Hilde J. P. Weerts",
    "Andreas C. Mueller",
    "Joaquin Vanschoren"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "The performance of many machine learning algorithms depends on their hyperparameter settings. The goal of this study is to determine whether it is important to tune a hyperparameter or whether it can be safely set to a default value. We present a methodology to determine the importance of tuning a hyperparameter based on a non-inferiority test and tuning risk: the performance loss that is incurred when a hyperparameter is not tuned, but set to a default value. Because our methods require the notion of a default parameter, we present a simple procedure that can be used to determine reasonable default parameters. We apply our methods in a benchmark study using 59 datasets from OpenML. Our results show that leaving particular hyperparameters at their default value is non-inferior to tuning these hyperparameters. In some cases, leaving the hyperparameter at its default value even outperforms tuning it using a search procedure with a limited number of iterations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-07-15T10:06:59.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning algorithms",
      "default value",
      "tuning hyperparameters",
      "importance",
      "determine reasonable default parameters"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}