feat(ml): implement training stage with MLflow tracking and model wrappers

- Create RandomForestModel and XGBoostModel wrappers with class weight support
- Implement temporal and random train/val/test splitting
- Add MLflow experiment tracking with full parameter and metric logging
- Create evaluation module for confusion matrix, feature importance, and classification reports
- Implement model training with sklearn/xgboost flavor logging and optional registry registration
- Store training run metadata in PostgreSQL
- Wire training stage into pipeline.py orchestrator
- Support both RandomForest and XGBoost models with configurable hyperparameters

openspec/changes/candle-backend/tasks.md

@@ -36,18 +36,18 @@
 
 ## 5. Training Stage
 
-- [ ] 5.1 Create `services/ml/training/train.py` — main training entry point: load labeled CSV, split, train, evaluate, log to MLflow
-- [ ] 5.2 Implement temporal train/validation/test splitting with configurable ratios, warn on random split
-- [ ] 5.3 Create `services/ml/training/models/random_forest.py` — RandomForestClassifier wrapper with class_weights support
-- [ ] 5.4 Create `services/ml/training/models/xgboost_model.py` — XGBClassifier wrapper with class_weights support
-- [ ] 5.5 Implement model dispatch — select model class based on `model_type` config, fail with supported types list for unknown types
-- [ ] 5.6 Implement MLflow experiment tracking — create run, log config artifact, dataset params, per-class sample counts, all hyperparameters
-- [ ] 5.7 Implement metrics logging — accuracy, f1_macro, f1_weighted, per-class precision/recall/F1
-- [ ] 5.8 Create `services/ml/training/evaluation.py` — generate confusion matrix plot, feature importance plot, classification report text
-- [ ] 5.9 Implement MLflow artifact logging — log confusion_matrix.png, feature_importance.png, classification_report.txt, pipeline_config.yaml
-- [ ] 5.10 Implement MLflow model registration — log model with sklearn/xgboost flavor, register in registry if configured
-- [ ] 5.11 Store training run metadata in PostgreSQL `training_runs` table
-- [ ] 5.12 Wire training into `pipeline.py`
+- [x] 5.1 Create `services/ml/training/train.py` — main training entry point: load labeled CSV, split, train, evaluate, log to MLflow
+- [x] 5.2 Implement temporal train/validation/test splitting with configurable ratios, warn on random split
+- [x] 5.3 Create `services/ml/training/models/random_forest.py` — RandomForestClassifier wrapper with class_weights support
+- [x] 5.4 Create `services/ml/training/models/xgboost_model.py` — XGBClassifier wrapper with class_weights support
+- [x] 5.5 Implement model dispatch — select model class based on `model_type` config, fail with supported types list for unknown types
+- [x] 5.6 Implement MLflow experiment tracking — create run, log config artifact, dataset params, per-class sample counts, all hyperparameters
+- [x] 5.7 Implement metrics logging — accuracy, f1_macro, f1_weighted, per-class precision/recall/F1
+- [x] 5.8 Create `services/ml/training/evaluation.py` — generate confusion matrix plot, feature importance plot, classification report text
+- [x] 5.9 Implement MLflow artifact logging — log confusion_matrix.png, feature_importance.png, classification_report.txt, pipeline_config.yaml
+- [x] 5.10 Implement MLflow model registration — log model with sklearn/xgboost flavor, register in registry if configured
+- [x] 5.11 Store training run metadata in PostgreSQL `training_runs` table
+- [x] 5.12 Wire training into `pipeline.py`
 
 ## 6. Inference Service (FastAPI)