feat: update active LGBM parameters and training log with new metrics

- Updated active LGBM parameters with new timestamp, trial results, and model configurations to reflect recent training outcomes.
- Added new entries to the training log, capturing detailed metrics including AUC, precision, recall, and tuned parameters for the latest model iterations.

This update enhances the tracking of model performance and parameter tuning in the ML pipeline.

models/training_log.json

@@ -351,5 +351,55 @@
       "reg_lambda": 0.000157
     },
     "weight_scale": 1.783105
+  },
+  {
+    "date": "2026-03-03T00:13:56.456518",
+    "backend": "lgbm",
+    "auc": 0.9439,
+    "best_threshold": 0.6558,
+    "best_precision": 0.667,
+    "best_recall": 0.154,
+    "samples": 1524,
+    "features": 23,
+    "time_weight_decay": 2.0,
+    "model_path": "models/lgbm_filter.pkl",
+    "tuned_params_path": null,
+    "lgbm_params": {
+      "n_estimators": 434,
+      "learning_rate": 0.123659,
+      "max_depth": 6,
+      "num_leaves": 14,
+      "min_child_samples": 10,
+      "subsample": 0.929062,
+      "colsample_bytree": 0.94633,
+      "reg_alpha": 0.573971,
+      "reg_lambda": 0.000157
+    },
+    "weight_scale": 1.783105
+  },
+  {
+    "date": "2026-03-03T00:20:43.712971",
+    "backend": "lgbm",
+    "auc": 0.9473,
+    "best_threshold": 0.3015,
+    "best_precision": 0.465,
+    "best_recall": 0.769,
+    "samples": 1524,
+    "features": 23,
+    "time_weight_decay": 0.5,
+    "model_path": "models/lgbm_filter.pkl",
+    "tuned_params_path": "models/active_lgbm_params.json",
+    "lgbm_params": {
+      "n_estimators": 195,
+      "learning_rate": 0.033934,
+      "max_depth": 3,
+      "num_leaves": 7,
+      "min_child_samples": 11,
+      "subsample": 0.998659,
+      "colsample_bytree": 0.837233,
+      "reg_alpha": 0.007008,
+      "reg_lambda": 0.80039
+    },
+    "weight_scale": 0.718348
   }
 ]