code: def cupMediumGBM():
      train = h2o.import_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/usecases/cup98LRN_z.csv"
          )
      )
      test = h2o.import_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/usecases/cup98VAL_z.csv"
          )
      )
  
      train["TARGET_B"] = train["TARGET_B"].asfactor()
  
      # Train H2O GBM Model:
      train_cols = train.names
      for c in ["C1", "TARGET_D", "TARGET_B", "CONTROLN"]:
          train_cols.remove(c)
  
      model = H2OGradientBoostingEstimator(
          distribution="bernoulli", ntrees=5
      )
      model.train(x=train_cols, y="TARGET_B", training_frame=train)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: cupMediumGBM() without return types