DevOps Engineer
|
6b762068fd
|
feat(ml): train ensemble model and generate benchmark report
Results:
- XGBoost (Optuna 100 trials): AUC=0.7856, Precision@3=0.5783
- LightGBM (Optuna 100 trials): AUC=0.7833, Precision@3=0.5736
- MLP (3 layers 256-128-64): AUC=0.7743, Precision@3=0.5643
- Ensemble (weighted voting): AUC=0.7840, Precision@3=0.5814
Baseline XGBoost: Precision@3=0.5287
Delta: +0.0527 (+5.3%) — DEPLOY threshold met (+5%)
Latency: 35ms/race, 69ms/full-day (well under 200ms limit)
SHAP: 31/43 features selected, top features: rang_cote,
implied_prob, cote_direct, ratio_cote_field
All 12 regression/latency tests passing.
Co-Authored-By: Paperclip <noreply@paperclip.ing>
|
2026-04-25 19:10:41 +02:00 |
|
DevOps Engineer
|
0e7bcff6b0
|
feat(ml): add ensemble XGBoost+LightGBM+MLP with Optuna optimization
- 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>
|
2026-04-25 18:18:48 +02:00 |
|