Marko Djordjevic f4c0f9a836 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		2026-02-15 14:22:19 +01:00
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archive	span annotations are working	2026-02-15 10:16:05 +01:00
candle-backend	feat(ml): implement training stage with MLflow tracking and model wrappers	2026-02-15 14:22:19 +01:00