6b762068fd
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>
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1.5 KiB
Benchmark ML Ensemble — Turf Prédictions
Date: 2026-04-25
Dataset: 10,899 partants
Holdout: 2,180 lignes (2026-04-19 → 2026-04-24)
Résultats
| Modèle | Precision@3 | AUC | Latence/prédiction |
|---|---|---|---|
| XGBoost (baseline) | 0.5287 | 0.7254 | — |
| xgboost | 0.5783 | 0.7856 | 0.01 ms |
| lightgbm | 0.5736 | 0.7833 | 0.00 ms |
| mlp | 0.5643 | 0.7743 | 0.01 ms |
| Ensemble | 0.5814 | 0.7840 | 0.02 ms |
Décision de déploiement
- Delta Precision@3 : +0.0527 (+5.3%)
- Seuil requis : +5%
- Résultat : ✅ DEPLOIEMENT RECOMMANDE
Optimisation Optuna
- Trials XGBoost : 100
- Trials LightGBM : 100
- Pruning : MedianPruner
Meilleurs hyperparamètres XGBoost
{
"n_estimators": 141,
"max_depth": 5,
"learning_rate": 0.016298172447266404,
"subsample": 0.7660470794373848,
"colsample_bytree": 0.471124415020467,
"min_child_weight": 14,
"reg_alpha": 1.9364166463791586,
"reg_lambda": 6.018030083488602,
"gamma": 4.614943551368141
}
Meilleurs hyperparamètres LightGBM
{
"n_estimators": 186,
"max_depth": 4,
"learning_rate": 0.012915117465216954,
"num_leaves": 141,
"subsample": 0.6193119116922561,
"colsample_bytree": 0.539310022549326,
"min_child_samples": 9,
"reg_alpha": 0.6864583098112754,
"reg_lambda": 0.0549259590914184
}
Features
- Total features : 43
- Retenues par SHAP : 31
Poids de l'ensemble
- xgboost : 0.2316
- lightgbm : 0.2342
- mlp : 0.2129