Joint Bayesian Optimization Results
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Timestamp: 20261013_191427
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: 3459

L0_a Hyperparameters (XGBOOST):
  n_estimators: 353
  learning_rate: 0.08593352403966907
  max_depth: 5
  min_child_weight: 10
  colsample_bytree: 0.8409762491970826
  subsample: 0.6131215724266281
  reg_alpha: 4.570519385409738e-07
  reg_lambda: 2.8283894986165263

L0_b Hyperparameters (XGBOOST):
  n_estimators: 315
  learning_rate: 0.012585705705476314
  max_depth: 8
  min_child_weight: 7
  colsample_bytree: 0.7899752091786167
  subsample: 0.6208131853940166
  reg_alpha: 9.841096615908965e-08
  reg_lambda: 7.535238673959664e-07

L1_a Hyperparameters (XGBOOST):
  n_estimators: 211
  learning_rate: 0.06418916831693935
  max_depth: 9
  min_child_weight: 4
  colsample_bytree: 0.7824935442944038
  subsample: 0.7807886365971736
  reg_alpha: 3.2750794646673485e-05
  reg_lambda: 1.7569452511396043e-07

