code: def cric__gbm():
      """Gradient Boosted Trees"""
      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
      )
  
      return model
  
imports: import numpy
  import sklearn
  import sklearn.ensemble
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 17 for calling a global function with example usage: cric__gbm() and returns: model