{
  "manifest_version": "synthetic-evaluation-evidence-v1",
  "prepared_date": "2026-09-20",
  "data_origin": "Original fictional conditional account snapshots generated by make_fixture.py; no real account records or continuous event-log reconstruction.",
  "generator_seed": 20261004,
  "rows": 320,
  "customers": 40,
  "prediction_grain": "one customer at one decision timestamp",
  "target_contract": {
    "version": "synthetic-inactivity-v1",
    "population": "40 fictional existing accounts scored at eight fixed UTC decision times",
    "target": "inactive_next_7d",
    "target_definition": "1 if active days in [decision_at, horizon_end) equal zero; otherwise 0",
    "horizon_elapsed_days": 7,
    "label_reporting_delay_days": 1,
    "features": [
      "days_since_activity",
      "tickets_7d",
      "tenure_days",
      "plan"
    ],
    "forbidden_predictors": [
      "snapshot_id",
      "customer_id",
      "decision_at",
      "features_available_at",
      "horizon_end",
      "label_available_at",
      "future_active_days",
      "inactive_next_7d",
      "split"
    ],
    "train_freeze": "2026-02-01T00:00:00Z",
    "selection_freeze": "2026-03-01T00:00:00Z",
    "evaluation_as_of": "2026-03-20T00:00:00Z",
    "candidate_C": [
      0.1,
      1.0,
      10.0
    ],
    "selection_rule": "Lowest validation log loss; exact ties prefer lower C. Fit on training only, with no train+validation refit for this reference.",
    "classification_threshold": 0.5,
    "threshold_rule": "positive when probability >= threshold",
    "baseline": "DummyClassifier(strategy='prior') fitted on the same training targets",
    "seed": 20261004,
    "scope": "Synthetic predictive teaching experiment; not contractual churn, intervention uplift or production performance."
  },
  "input_columns": [
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    "customer_id",
    "decision_at",
    "features_available_at",
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    "plan",
    "horizon_end",
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}