Joint Bayesian Optimization Results
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Timestamp: 20261017_003348
Best optimization objective (mean_skill_huber): 0.0000

Model Configuration:
  L0_a: XGBOOST, target_transform=standard, loss=none
  L0_b: XGBOOST, target_transform=standard, loss=none
  L1_a: XGBOOST, target_transform=standard, loss=none

XGBoost device: cpu
Inner CV stratification: GMM on training-set groups only
Sample weights: fit_weight_source=weighted [weighted_fit_weight], use_pd_weight=True
Parsimony: mode=off, strength=0.0

Pruning Parameters:
  Redundancy %: 0
  Error %: 0
  Error recursions: 0
  Samples removed: 0
  Samples kept: 3541

L0_a Hyperparameters (XGBOOST):
  n_estimators: 256
  learning_rate: 0.1119258078463101
  max_depth: 7
  min_child_weight: 10
  colsample_bytree: 0.9768894771958613
  subsample: 0.6364562041301022
  reg_alpha: 0.007815405509313922
  reg_lambda: 9.60330235769479

L0_b Hyperparameters (XGBOOST):
  n_estimators: 178
  learning_rate: 0.027808517442339597
  max_depth: 7
  min_child_weight: 8
  colsample_bytree: 0.8301848076177748
  subsample: 0.8099736580982339
  reg_alpha: 0.03706757930583534
  reg_lambda: 4.894044814706982

L1_a Hyperparameters (XGBOOST):
  n_estimators: 438
  learning_rate: 0.054633321401713425
  max_depth: 9
  min_child_weight: 8
  colsample_bytree: 0.9502681117057538
  subsample: 0.6259049048913676
  reg_alpha: 0.022959695678682474
  reg_lambda: 6.844918836574261e-07

