code: def swpredsRF():
      # Training set has two predictor columns
      # X1: 10 categorical levels, 100 observations per level; X2: Unif(0,1) noise
      # Ratio of y = 1 per Level: cat01 = 1.0 (strong predictor), cat02 to cat10 = 0.5 (weak predictors)
  
      # Log.info("Importing swpreds_1000x3.csv data...\n")
      swpreds = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/swpreds_1000x3.csv"
          )
      )
      swpreds["y"] = swpreds["y"].asfactor()
  
      # Log.info("Summary of swpreds_1000x3.csv from H2O:\n")
      # swpreds.summary()
  
      # Train H2O DRF without Noise Column
  
      # Log.info("Distributed Random Forest with only Predictor Column")
      model1 = H2ORandomForestEstimator(
          ntrees=50, max_depth=20, nbins=500
      )
      model1.train(x="X1", y="y", training_frame=swpreds)
      model1.show()
      perf1 = model1.model_performance(swpreds)
      print(perf1.auc())
  
      # Train H2O DRF Model including Noise Column:
      # Log.info("Distributed Random Forest including Noise Column")
      model2 = H2ORandomForestEstimator(
          ntrees=50, max_depth=20, nbins=500
      )
      model2.train(x=["X1", "X2"], y="y", training_frame=swpreds)
      model2.show()
      perf2 = model2.model_performance(swpreds)
      print(perf2.auc())
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: swpredsRF() without return types