Upload training_config.json

training_config.json

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+{
+  "model_name": "CBC Credit-Card Fraud Classifier",
+  "hf_repo": "careerbytecode/mlops-ref-finance-fraud",
+  "task": "binary classification (credit-card fraud detection, imbalanced 0.17%)",
+  "model_type": "XGBoost (Optuna-tuned, scale_pos_weight), time-aware split",
+  "framework": "xgboost",
+  "serialization": "joblib",
+  "loader": "joblib.load -> XGBClassifier; call .predict_proba(DataFrame[FEATURES])[:, 1], flag if >= recommended_threshold",
+  "random_state": 42,
+  "scale_pos_weight": 498.3863,
+  "best_params": {
+    "n_estimators": 400,
+    "learning_rate": 0.1636812394286703,
+    "max_depth": 4,
+    "min_child_weight": 5,
+    "subsample": 0.6587788742440184
+  },
+  "feature_columns": [
+    "V1",
+    "V2",
+    "V3",
+    "V4",
+    "V5",
+    "V6",
+    "V7",
+    "V8",
+    "V9",
+    "V10",
+    "V11",
+    "V12",
+    "V13",
+    "V14",
+    "V15",
+    "V16",
+    "V17",
+    "V18",
+    "V19",
+    "V20",
+    "V21",
+    "V22",
+    "V23",
+    "V24",
+    "V25",
+    "V26",
+    "V27",
+    "V28",
+    "Amount"
+  ],
+  "split": {
+    "trainfit": 182276,
+    "val": 45569,
+    "test": 56962,
+    "method": "time-aware (latest 20% by Time = test)"
+  },
+  "dataset": "ULB mlg-ulb credit-card fraud (284,807 tx, 492 frauds), ODbL/DbCL",
+  "python_version": "3.14.4",
+  "library_versions": {
+    "scikit-learn": "1.8.0",
+    "xgboost": "3.2.0",
+    "numpy": "2.4.6",
+    "pandas": "2.3.3",
+    "joblib": "1.5.3"
+  },
+  "training_date": "2026-06-04T20:25:08.260852+00:00"
+}