{
  "schema_version": "1.0",
  "study_id": "S47",
  "run_id": "S47-a0ada992-696c3a18",
  "code_version": "a0ada99288b53cae3fd494991e16e055c7c5c39a",
  "source_sha256": {
    "research_program/__init__.py": "5abb4829b477fd3068ee4c11dad2b59cd33e90aad06a17a5e9fb2f840a75abf1",
    "research_program/evaluation.py": "09a5d060c34b8cd1e5a3330d90f5ff3e12adbca8cfe1ebe3297da2b91d227e45",
    "research_program/io.py": "16de123c16d80bce5eb8d6766224dec1729d7c00449c2528f2348d86ad68d35f",
    "research_program/validate.py": "3e6a591e2b77799bdf73683db438b2998e2e457340626c98b7ceed510ab3329e",
    "studies/S47/config.json": "86a9730bb7e7ba97a55b467a3b95b8ceebf9bec5b03a72627bdef33280f1e1cc",
    "studies/S47/ingest.py": "c9ed9f8948cc49a6b522fd21465c0b08e14b059fd909ea18a20dad86661ce11e",
    "studies/S47/models.py": "bdacbec5f1d0a71b51c8711a9365e3c7c41bb0435965cd41e22e1237a790b596",
    "studies/S47/study.py": "6bade19f9137faa04e071f489a643c0084e5e04370c1b9ea9032c1663aafb042",
    "uv.lock": "5985583acc764c8b04a4a783eace913b25f13c19b338039e96abcb1ccf7524bf"
  },
  "evaluated_on": "2026-09-29",
  "data": {
    "source_url": "https://archive.ics.uci.edu/dataset/563/iranian+churn+dataset",
    "release": "Official UCI Iranian Churn CSV retrieved September 29, 2026",
    "sha256": "696c3a1812267980751f30d19193a1b430ac4ad76172bb089e664c96813ead66",
    "csv_sha256": "90d5fb6bd1630cd4de4b4d28fcf8b4cb92a8f6ab7484605b0799d47386f7dbe1",
    "license": "CC BY 4.0",
    "citation": "Iranian Churn (2020). UCI Machine Learning Repository. doi:10.24432/C5JW3Z."
  },
  "target": {
    "label": "Churn at the end of month twelve",
    "estimand": "Historical row-level risk among the sampled telecom profiles",
    "prediction_cutoff": "Publisher-described first-nine-month aggregate features; source timestamps unavailable",
    "horizon": "Three-month documented planning gap",
    "unit": "Source row, with identical primary-feature profiles grouped across every split"
  },
  "cohort": "Fixed grouped stratified final partition; no out-of-time or verified individual-customer validation",
  "samples": {
    "total": 3150,
    "test": 624,
    "test_profiles": 561,
    "test_churn": 94,
    "development": 1897,
    "development_churn": 302,
    "calibration": 629,
    "calibration_churn": 99
  },
  "models": [
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      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "core/boosting",
      "label": "Histogram boosting · core",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "core/linear",
      "label": "Ridge logistic · core",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "no_friction/additive",
      "label": "Additive logistic · no_friction",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "no_friction/boosting",
      "label": "Histogram boosting · no_friction",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_status/additive",
      "label": "Additive logistic · with_status",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_status/boosting",
      "label": "Histogram boosting · with_status",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_value/additive",
      "label": "Additive logistic · with_value",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_value/boosting",
      "label": "Histogram boosting · with_value",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_both/additive",
      "label": "Additive logistic · with_both",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "with_both/boosting",
      "label": "Histogram boosting · with_both",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "core/prevalence",
      "label": "Development prevalence · core",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    },
    {
      "id": "core/service_rule",
      "label": "Complaint / low-usage rule · core",
      "specification": "Frozen group-separated model, variant-specific selection and reserved probability calibration; see PROTOCOL.md."
    }
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      "estimate": 0.50633662,
      "lower": 0.40649391,
      "upper": 0.60862045,
      "confidence": 0.95,
      "unit": "proportion",
      "split": "final"
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      "upper": 0.8621429,
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    {
      "model": "core/service_rule",
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      "lower": 0.83627052,
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      "unit": "ratio",
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  ],
  "uncertainty": "1,000 paired primary-profile bootstrap draws; 95% percentile intervals condition on the fitted model. Capacity ranking is recomputed per draw. Additive effect stability refits 100 profile-bootstrap development samples using a fixed calibration map.",
  "assumptions": [
    "Publisher timing places predictors before the three-month planning gap; no timestamps independently establish this.",
    "Identical primary profiles are kept together as a conservative dependence proxy; actual identities are absent.",
    "Prospective sample-size controls are assumptions for an unrun randomized trial, not estimated campaign effects."
  ],
  "limitations": [
    "Small single-company sample with unspecified collection year and no temporal replication.",
    "No actual customer ID; repetitions and conflicting identical profiles prevent verified customer independence.",
    "Status and calculated customer value have insufficiently documented semantics; their sensitivity gains do not prove operational availability.",
    "Age is a category representative, and charge amount includes seven values beyond the dictionary range.",
    "Associations and model-risk contrasts do not identify benefits from resolving complaints or changing usage.",
    "Sparse contractual churn and correlated features limit subgroup and effect interpretation.",
    "Independent technical review is pending."
  ],
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