{
  "title": "Independent Random Forest baseline",
  "evaluated_at": "2026-10-03T05:26:33.810612+00:00",
  "dataset": {
    "name": "Pima Indians Diabetes",
    "url": "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv",
    "sha256": "6bfe5d0f379d17a0e0819b996407e3c09bf80febd4287f2ed212190dfff154af",
    "rows": 768,
    "train_rows": 614,
    "test_rows": 154,
    "features": 8
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  "method": "Stratified 80/20 split, seed 42. Invalid zero values become missing. Median imputation fitted only on training rows. Fixed Random Forest parameters, threshold 0.5. No hyperparameter search or resampling.",
  "model_parameters": {
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    "max_depth": 8,
    "min_samples_leaf": 2,
    "max_features": "sqrt",
    "class_weight": "balanced",
    "random_state": 42,
    "n_jobs": 1
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  "versions": {
    "python": "3.12.14",
    "sklearn": "1.9.1",
    "pandas": "3.0.1",
    "numpy": "2.3.5"
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  "related_project_commit": "67f96a8913365a65f288d57d87ad03ffe0a9174e",
  "scope": "New baseline run for the portfolio, not the deployed clinical model or a reproduction of its README metrics. Uses eight raw features and training-only median imputation instead of the project outcome-conditioned imputation.",
  "limitations": [
    "One small held-out split from a specific population; no clinical validation.",
    "The 95% interval bootstraps test rows conditional on this fitted model; it does not include training or split uncertainty.",
    "No claim of fairness, prospective performance, or generalization to other populations."
  ]
}