{
  "slug": "smote",
  "term": "SMOTE",
  "aliases": [
    "Synthetic Minority Over-sampling Technique",
    "synthetic minority oversampling"
  ],
  "definition": "SMOTE is an oversampling method that creates synthetic minority-class feature vectors by interpolating between a minority example and selected minority-class nearest neighbors.",
  "note": "For numeric features, the original construction uses x_new = x + u * (x_neighbor - x), with u drawn between 0 and 1; it creates feature-space samples, not new observed drive failures. [chawla2002] Fit resampling inside each training fold, leaving validation and test observations unresampled; resampling before splitting can leak information and distort evaluation prevalence. [imblearn-pitfalls] A sampler pipeline does not itself establish an appropriate chronological or device-group split for storage telemetry. [hesela-symptoms]",
  "status": "shipping",
  "advantages": [],
  "limitations": [
    {
      "text": "Interpolation assumes a meaningful feature representation and neighborhood; a synthetic SMART vector is not evidence that a physical drive followed that trajectory.",
      "citationIds": [
        "chawla2002"
      ]
    },
    {
      "text": "Oversampling is a training choice, not a validation protocol or a guarantee of improved deployed precision.",
      "citationIds": [
        "imblearn-pitfalls",
        "hesela-symptoms"
      ]
    }
  ],
  "related": [
    "class-prevalence",
    "precision",
    "recall",
    "smart"
  ],
  "supersedes": [],
  "supersededBy": [],
  "firstSeen": null,
  "lastSeen": null,
  "updated": "2026-10-13",
  "citations": [
    {
      "id": "chawla2002",
      "title": "Chawla, Bowyer, Hall and Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. JAIR 16 (2002), 321-357, Section 4.2.",
      "url": "https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume16/chawla02a-html/node6.html",
      "accessed": "2026-10-13"
    },
    {
      "id": "imblearn-pitfalls",
      "title": "imbalanced-learn 0.14: Common pitfalls and recommended practices, Section 9.1, resampling leakage.",
      "url": "https://imbalanced-learn.org/0.14/common_pitfalls.html",
      "accessed": "2026-10-13"
    },
    {
      "id": "hesela-symptoms",
      "title": "Hesela: SSD symptoms, classification and forward-time evaluation boundaries.",
      "url": "https://hesela.com/analyses/ssd-failures-datacenter-symptoms/#classification-boundary",
      "accessed": "2026-10-13"
    }
  ]
}