improve model training

docs/rain_model_runbook.md

@@ -27,6 +27,7 @@ python scripts/train_rain_model.py \
   --tune-hyperparameters \
   --max-hyperparam-trials 12 \
   --calibration-methods "none,sigmoid,isotonic" \
+  --threshold-policy "walk_forward" \
   --walk-forward-folds 4 \
   --model-version "rain-auto-v1-extended" \
   --out "models/rain_model.pkl" \
@@ -40,6 +41,7 @@ Review in report:
 - `candidate_models[*].calibration_comparison`
 - `naive_baselines_test`
 - `sliced_performance_test`
+- `threshold_tuning_walk_forward`
 - `walk_forward_backtest`
 
 ## 3) Deploy
@@ -133,6 +135,7 @@ The script exits non-zero on failure, so it can directly drive alerting.
 - `RAIN_TUNE_HYPERPARAMETERS`
 - `RAIN_MAX_HYPERPARAM_TRIALS`
 - `RAIN_CALIBRATION_METHODS`
+- `RAIN_THRESHOLD_POLICY`
 - `RAIN_WALK_FORWARD_FOLDS`
 - `RAIN_ALLOW_EMPTY_DATA`
 - `RAIN_MODEL_BACKUP_PATH`
@@ -141,3 +144,15 @@ The script exits non-zero on failure, so it can directly drive alerting.
 Recommended production defaults:
 - Enable tuning daily or weekly (`RAIN_TUNE_HYPERPARAMETERS=true`)
 - Keep walk-forward folds `0` in continuous mode, run fold backtests in scheduled evaluation jobs
+
+## 8) Auto-Recommend Candidate
+
+To compare saved training reports and pick a deployment candidate automatically:
+
+```sh
+python scripts/recommend_rain_model.py \
+  --reports-glob "models/rain_model_report*.json" \
+  --require-walk-forward \
+  --top-k 5 \
+  --json-out "models/rain_model_recommendation.json"
+```