old_contents: #!/usr/bin/env python
  
  from h2o import H2OFrame
  import h2o as h2o
  
  df = H2OFrame.from_csv("allyears_tiny.csv", index_col = False)
  print(df.head())
  
  air['RandNum'] = df.random.uniform()
  print(air.head())
  
  air_train = air.ix[air['RandNum'] <= 0.8]
  air_valid = air.ix[(air['RandNum'] > 0.8) & (air['RandNum'] <= 0.9)]
  air_test  = air.ix[air['RandNum'] > 0.9]
  
  myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "Month", "DayofMonth", "DayOfWeek"]
  myY = "IsDepDelayed"
  
  air_gbm = h2o.gbm(x = myX, y = myY, data = air_train, validation = air_valid,
                    distribution = "multinomial",
                    n_trees = 10, interaction_depth = 3, shrinkage = 0.01,
                    importance = True)
  print(air_gbm)
  
  pred = h2o.predict(air_gbm, air_test)
  print(pred.head())
  
new_contents: #!/usr/bin/env python
  
  from h2o import H2OFrame
  import h2o as h2o
  
  localH2O <- h2o.init()
  air = H2OFrame.from_csv(localH2O, "allyears_tiny.csv", index_col = False)
  print(air.head())
  
  air['RandNum'] = air.random.uniform()
  print(air.head())
  
  air_train = air.ix[air['RandNum'] <= 0.8]
  air_valid = air.ix[(air['RandNum'] > 0.8) & (air['RandNum'] <= 0.9)]
  air_test  = air.ix[air['RandNum'] > 0.9]
  
  myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "Month", "DayofMonth", "DayOfWeek"]
  myY = "IsDepDelayed"
  
  air_gbm = h2o.gbm(x = myX, y = myY, data = air_train, validation = air_valid,
                    distribution = "multinomial",
                    n_trees = 10, interaction_depth = 3, shrinkage = 0.01,
                    importance = True)
  print(air_gbm)
  
  pred = h2o.predict(air_gbm, air_test)
  print(pred.head())
  
subject: Add localH2O.