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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.dvc	feat(ml): add database schema, config parser, and DVC setup	2026-02-15 12:08:53 +01:00
app	feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns	2026-02-15 12:28:58 +01:00
candle_ml.egg-info	feat(ml): add database schema, config parser, and DVC setup	2026-02-15 12:08:53 +01:00
config	feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup	2026-02-15 11:58:31 +01:00
features	feat(ml): implement feature engineering pipeline	2026-02-15 12:22:59 +01:00
migrations	feat(ml): add database schema, config parser, and DVC setup	2026-02-15 12:08:53 +01:00
training	feat(ml): implement training stage with MLflow tracking and model wrappers	2026-02-15 14:22:19 +01:00
.dvcignore	feat(ml): add database schema, config parser, and DVC setup	2026-02-15 12:08:53 +01:00
Dockerfile	feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup	2026-02-15 11:58:31 +01:00
pipeline.py	feat(ml): implement training stage with MLflow tracking and model wrappers	2026-02-15 14:22:19 +01:00
pyproject.toml	feat(ml): add database schema, config parser, and DVC setup	2026-02-15 12:08:53 +01:00