code: def bigcatRF():
      # Training set has 100 categories from cat001 to cat100
      # Categories cat001, cat003, ... are perfect predictors of y = 1
      # Categories cat002, cat004, ... are perfect predictors of y = 0
  
      # Log.info("Importing bigcat_5000x2.csv data...\n")
      bigcat = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/bigcat_5000x2.csv"
          )
      )
      bigcat["y"] = bigcat["y"].asfactor()
  
      # Log.info("Summary of bigcat_5000x2.csv from H2O:\n")
      # bigcat.summary()
  
      # Train H2O DRF Model:
      # Log.info("H2O DRF (Naive Split) with parameters:\nclassification = TRUE, ntree = 1, depth = 1, nbins = 100, nbins_cats=10\n")
      model = H2ORandomForestEstimator(
          ntrees=1, max_depth=1, nbins=100, nbins_cats=10
      )
      model.train(x="X", y="y", training_frame=bigcat)
      model.show()
  
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: bigcatRF() without return types