code: def cric__gbm():
      
      import xgboost
  
      # max_depth and subsample match the params used for the full cric data in the paper
      # learning_rate was set a bit higher to allow for faster runtimes
      # n_estimators was chosen based on a train/test split of the data
      model = xgboost.XGBClassifier(max_depth=5, n_estimators=400, learning_rate=0.01, subsample=0.2, n_jobs=8, random_state=0)
      
      # we want to explain the margin, not the transformed probability outputs
      model.__orig_predict = model.predict
      model.predict = lambda X: model.__orig_predict(X, output_margin=True) # pylint: disable=E1123
  
      return model
docs: Gradient Boosted Trees