{
  "id": "1907.04669",
  "version": "v1",
  "published": "2019-07-08T06:59:05.000Z",
  "updated": "2019-07-08T06:59:05.000Z",
  "title": "Optimal Explanations of Linear Models",
  "authors": [
    "Dimitris Bertsimas",
    "Arthur Delarue",
    "Patrick Jaillet",
    "Sebastien Martin"
  ],
  "comment": "arXiv admin note: substantial text overlap with arXiv:1907.03419",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpretability itself is not well-defined. We propose a general optimization framework to create explanations for linear models. Our methodology decomposes a linear model into a sequence of models of increasing complexity using coordinate updates on the coefficients. Computing this decomposition optimally is a difficult optimization problem for which we propose exact algorithms and scalable heuristics. By solving this problem, we can derive a parametrized family of interpretability metrics for linear models that generalizes typical proxies, and study the tradeoff between interpretability and predictive accuracy.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-08T06:59:05.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "linear model",
      "optimal explanations",
      "expose hidden biases",
      "general optimization framework",
      "important decisions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}