{
  "id": 11339941,
  "title": "Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research",
  "url": "https://urgent.news/2026/10/01/model-validation-in-machine-learning-a-scenario-based-guide-from-hold",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-01T08:23:58.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.01284v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stable result. This tutorial reviews hold-out validation, train/validation/test designs, repeated random subsampling, k-fold and repeated stratified cross-validation, leave-one-out and leave-p-out schemes, group-aware validation, and nested group…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}