{
  "id": "1605.02541",
  "version": "v1",
  "published": "2016-05-09T11:46:26.000Z",
  "updated": "2016-05-09T11:46:26.000Z",
  "title": "Mean Absolute Percentage Error for regression models",
  "authors": [
    "Arnaud De Myttenaere",
    "Boris Golden",
    "Bénédicte Le Grand",
    "Fabrice Rossi"
  ],
  "journal": "Neurocomputing, Elsevier, 2016, Advances in artificial neural networks, machine learning and computational intelligence - Selected papers from the 23rd European Symposium on Artificial Neural Networks (ESANN 2015), 192, pp.38-48",
  "doi": "10.1016/j.neucom.2015.12.114",
  "categories": [
    "stat.ML"
  ],
  "abstract": "We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-05-09T11:46:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "mean absolute percentage error",
      "regression models",
      "mape kernel regression",
      "weighted mean absolute error",
      "optimal mape model"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "Elsevier"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}