models:
  logistic_regression:
    C: 0.4
    max_iter: 1000
    class_weight: balanced
    solver: lbfgs

  # High regularization path for stable hybrid ensemble (see stable_training.yaml)
  logistic_regression_stable:
    C: 0.01
    max_iter: 2000
    class_weight: balanced
    solver: lbfgs

  random_forest:
    n_estimators: 100
    max_depth: 10
    min_samples_split: 10
    min_samples_leaf: 5
    max_features: sqrt
    class_weight: balanced
    n_jobs: -1

  xgboost:
    n_estimators: 100
    max_depth: 3
    learning_rate: 0.1
    subsample: 0.8
    colsample_bytree: 0.8
    min_child_weight: 5
    reg_lambda: 1
    scale_pos_weight: 1

evaluation:
  primary_metric: f1_weighted
  metrics:
    - accuracy
    - f1_weighted
    - precision_weighted
    - recall_weighted
    - roc_auc