CatBoost:
  ensemble:
    learning_rate: 0.03
    depth: 6
    loss_function: Logloss
    random_seed: 0
    l2_leaf_reg: 3
    subsample: 0.9
    grow_policy: Lossguide  # SymmetricTree or Depthwise or Lossguide
    bagging_temperature: 1
    random_strength: 3
    min_data_in_leaf: 25
    iterations: 10000
    early_stopping_rounds: 50
    custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"]
    logging_level: 'Silent'  # or 'Verbose', 'Info', 'Debug'
    train_dir: '/tmp'         # avoid write permission issues
    auto_class_weights: Balanced  # or None, or SqrtBalanced

  single_model:
    # in this mode, the model is trained on the entire dataset using the best_iter obtained from cross-validation
    learning_rate: 0.03
    depth: 6
    loss_function: Logloss
    random_seed: 0
    l2_leaf_reg: 3
    subsample: 0.9
    grow_policy: Lossguide  # SymmetricTree or Depthwise or Lossguide
    bagging_temperature: 1
    random_strength: 3
    min_data_in_leaf: 25
    custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"]
    logging_level: 'Silent'  # or 'Verbose', 'Info', 'Debug'
    train_dir: '/tmp'         # avoid write permission issues
    auto_class_weights: Balanced  # or None, or SqrtBalanced