feat: enhance precision optimization in model training

- Introduced a new plan to modify the Optuna objective function to prioritize precision under a recall constraint of 0.35, improving model performance in scenarios where false positives are costly.
- Updated training scripts to implement precision-based metrics and adjusted the walk-forward cross-validation process to incorporate precision and recall calculations.
- Enhanced the active LGBM parameters and training log to reflect the new metrics and model configurations.
- Added a new design document outlining the implementation steps for the precision-focused optimization.

This update aims to refine the model's decision-making process by emphasizing precision, thereby reducing potential losses from false positives.

models/training_log.json

@@ -401,5 +401,30 @@
       "reg_lambda": 0.80039
     },
     "weight_scale": 0.718348
+  },
+  {
+    "date": "2026-03-03T00:39:05.427160",
+    "backend": "lgbm",
+    "auc": 0.9436,
+    "best_threshold": 0.3041,
+    "best_precision": 0.467,
+    "best_recall": 0.269,
+    "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": 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
   }
 ]