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cyberpowder_monotonic_hgbr_prb_predictor_v1.json

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+{
+  "model_name": "cyberpowder_monotonic_hgbr_prb_predictor",
+  "version": "1.0.0",
+  "training_date": "2026-04-22T18:35:35.318976Z",
+  "framework": "scikit-learn",
+  "dataset_source": "cyberpowder/cyberpowder-network-metrics",
+  "feature_columns": [
+    "CQI",
+    "target_throughput_mbps"
+  ],
+  "target_column": "required_min_prb_ratio",
+  "best_model": "HistGradientBoostingRegressor",
+  "best_params": {
+    "min_samples_leaf": 5,
+    "max_leaf_nodes": 31,
+    "max_iter": 250,
+    "max_depth": 5,
+    "loss": "squared_error",
+    "learning_rate": 0.05,
+    "l2_regularization": 0.0
+  },
+  "evaluation": [
+    {
+      "Model": "Polynomial regression (deg=2)",
+      "MAE_raw": 2.8417608620629395,
+      "RMSE_raw": 3.663040795948601,
+      "R2_raw": 0.9284117269746499,
+      "MAE_snapped": 1.8981818181818182,
+      "RMSE_snapped": 3.1657255379799087,
+      "Exact_match": 0.43636363636363634,
+      "Within_5_PRB": 0.8981818181818182
+    },
+    {
+      "Model": "Monotonic HistGradientBoostingRegressor",
+      "MAE_raw": 2.8133798791664173,
+      "RMSE_raw": 3.613181374050587,
+      "R2_raw": 0.9303473089297923,
+      "MAE_snapped": 2.2581818181818183,
+      "RMSE_snapped": 3.4021383649912673,
+      "Exact_match": 0.3709090909090909,
+      "Within_5_PRB": 0.8763636363636363
+    },
+    {
+      "Model": "Linear regression",
+      "MAE_raw": 6.464496074003753,
+      "RMSE_raw": 7.834591065331759,
+      "R2_raw": 0.6725152804379628,
+      "MAE_snapped": 4.945454545454545,
+      "RMSE_snapped": 6.851675309400946,
+      "Exact_match": 0.2581818181818182,
+      "Within_5_PRB": 0.6145454545454545
+    }
+  ],
+  "valid_prb_levels": [
+    50,
+    51,
+    52,
+    53,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77,
+    78,
+    79,
+    80,
+    81,
+    82,
+    83,
+    84,
+    85,
+    86,
+    87,
+    88,
+    89,
+    90,
+    91,
+    92,
+    93,
+    94,
+    95
+  ],
+  "lower_prb_bound": 50,
+  "upper_prb_bound": 95,
+  "postprocess": "clip to valid PRB bounds, then snap to nearest valid PRB level",
+  "notes": [
+    "Cleaning pipeline rebuilt from the first notebook.",
+    "Monotonic constraints: CQI -> decreasing PRB, throughput target -> increasing PRB.",
+    "Exported ONNX model returns raw regression output; deployment should apply the same snap_to_valid_prb post-processing."
+  ]
+}