feat(ml): add ensemble XGBoost+LightGBM+MLP with Optuna optimization

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- train_ensemble.py: full training pipeline with 100-trial Optuna studies
  for XGBoost and LightGBM, MLP (256-128-64), SHAP feature selection,
  weighted soft-voting ensemble, benchmark report generation
- predict_v2.py: production prediction module with model cache invalidation
- combined_api.py: add /api/v1/predictions, /api/v1/model/status,
  /api/v1/model/invalidate-cache endpoints using ensemble model
- tests/test_ml_ensemble.py: regression, latency and API tests

Baseline XGBoost Precision@3: 0.5287 (holdout 20% temporal)
Deploy threshold: +5% = 0.5551

Co-Authored-By: Paperclip <noreply@paperclip.ing>

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DevOps Engineer

2026-04-25 18:18:41 +02:00

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