---
  root: ./

  run_name: 2-phase #update for specific run name
  path_out:  tests/ModelOutput/ #root + folder 
  path_in:  predictions/ens/ #root + folder

  prediction:  examples/data/prediction.csv #root + folder
  targets:  examples/data/targets.csv #root + folder
  training:  examples/data/training.csv

  predictors: ["feature_1", "feature_2", "feature_3"]
  verbose: 1
  seed : 1 # random seed
  n_threads : 1 # how many cpu threads to use
  cv : 3

  ensemble_config: 
    classifier: True
    regressor: True
    m1: "rf"
    m2: "knn"
    m3: "xgb"

  upsample: False
  stratify: True

  reg_scoring:
   R2: r2
   MAE: neg_mean_absolute_error
   RMSE: neg_root_mean_squared_error

  clf_scoring:
   accuracy: balanced_accuracy

  param_grid:
    rf_param_grid:
      reg_param_grid:
        n_estimators: [100]
        max_features: [4]
        max_depth: [50]
        min_samples_leaf: [0.5]
        max_samples: [0.5]     
      clf_param_grid:
        n_estimators: [100]  
        max_depth: [50] 
        max_samples: [0.8]

    xgb_param_grid:
      reg_param_grid:  
        learning_rate: [0.05]
        n_estimators: [100]
        max_depth: [7]
        subsample: [0.8]  
        colsample_bytree: [0.5]
        gamma: [1] 
        reg_alpha: [0.1]   
      clf_param_grid:  
        learning_rate: [0.01]
        n_estimators: [100]
        max_depth: [4]
        subsample: [0.6]  
        colsample_bytree: [0.6]
        gamma: [1] 
        reg_alpha: [1]   

    knn_param_grid:
      reg_param_grid:  
        max_samples: [0.2]
        max_features: [0.2]
        estimator__leaf_size: [25]
        estimator__n_neighbors: [3]
      clf_param_grid:  
        max_samples: [0.2]
        max_features: [0.2]
        estimator__leaf_size: [25]
        estimator__n_neighbors: [3]

  knn_bagging_estimators: 3

