Joint Bayesian Optimization Results
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Timestamp: 20261028_105652
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: 3478

L0_a Hyperparameters (XGBOOST):
  n_estimators: 485
  learning_rate: 0.05235194341172527
  max_depth: 5
  min_child_weight: 10
  colsample_bytree: 0.8751410275674848
  subsample: 0.6546402377666434
  reg_alpha: 0.059094387536113555
  reg_lambda: 1.2245914392402368e-08

L0_b Hyperparameters (XGBOOST):
  n_estimators: 172
  learning_rate: 0.014640168991698357
  max_depth: 10
  min_child_weight: 6
  colsample_bytree: 0.7479832327377139
  subsample: 0.6481254699877317
  reg_alpha: 1.3860724579213418e-06
  reg_lambda: 1.9805651254646766e-07

L1_a Hyperparameters (XGBOOST):
  n_estimators: 251
  learning_rate: 0.06311987063379466
  max_depth: 10
  min_child_weight: 6
  colsample_bytree: 0.7188687881790117
  subsample: 0.8731075721529803
  reg_alpha: 1.0517205084279582
  reg_lambda: 0.0004727161678826917

