chore: update .gitignore, CLAUDE.md, training_log.json, and ml_filter.py

- Added .worktrees/ to .gitignore to prevent tracking of worktree files.
- Marked `optuna-precision-objective-plan` as completed in CLAUDE.md.
- Added new training log entry for a LightGBM model with updated parameters and performance metrics in training_log.json.
- Updated error handling in ml_filter.py to return False on prediction errors instead of True, improving the robustness of the ML filter.

.gitignore

@@ -12,4 +12,6 @@ models/tune_results_*.json
 data/*.parquet
 .worktrees/
 .DS_Store
-.cursor/
+.cursor/
+
+.worktrees/

CLAUDE.md

@@ -115,6 +115,6 @@ All design documents and implementation plans are stored in `docs/plans/` with t
 | 2026-03-02 | `hold-negative-sampling` (design + plan) | Completed |
 | 2026-03-03 | `position-monitor-logging` | Completed |
 | 2026-03-03 | `adx-ml-feature-migration` (design + plan) | Completed |
-| 2026-03-03 | `optuna-precision-objective-plan` | Pending |
+| 2026-03-03 | `optuna-precision-objective-plan` | Completed |
 | 2026-03-03 | `demo-1m-125x` (design + plan) | In Progress |
 | 2026-03-04 | `oi-derived-features` (design + plan) | Completed |

models/training_log.json

@@ -476,5 +476,30 @@
       "reg_lambda": 0.082872
     },
     "weight_scale": 1.431662
+  },
+  {
+    "date": "2026-03-05T02:00:24.489871",
+    "backend": "lgbm",
+    "auc": 0.9448,
+    "best_threshold": 0.3075,
+    "best_precision": 0.452,
+    "best_recall": 0.463,
+    "samples": 3246,
+    "features": 26,
+    "time_weight_decay": 2.0,
+    "model_path": "models/lgbm_filter.pkl",
+    "tuned_params_path": null,
+    "lgbm_params": {
+      "n_estimators": 221,
+      "learning_rate": 0.031072,
+      "max_depth": 5,
+      "num_leaves": 20,
+      "min_child_samples": 39,
+      "subsample": 0.83244,
+      "colsample_bytree": 0.526349,
+      "reg_alpha": 0.062177,
+      "reg_lambda": 0.082872
+    },
+    "weight_scale": 1.431662
   }
 ]

src/ml_filter.py

@@ -149,8 +149,8 @@ class MLFilter:
             )
             return bool(proba >= self._threshold)
         except Exception as e:
-            logger.warning(f"ML 필터 예측 오류 (폴백 허용): {e}")
+            logger.warning(f"ML 필터 예측 오류 (진입 차단): {e}")
-            return True
+            return False
 
     def reload_model(self):
         """외부에서 강제 리로드할 때 사용 (하위 호환)."""