code: def fiftycatRF():
      # Training set has only 45 categories cat1 through cat45
      # Log.info("Importing 50_cattest_train.csv data...\n")
      train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/50_cattest_train.csv"
          )
      )
      train["y"] = train["y"].asfactor()
  
      # Log.info("Summary of 50_cattest_train.csv from H2O:\n")
      # train.summary()
  
      # Train H2O DRF Model:
      # Log.info(paste("H2O DRF with parameters:\nclassification = TRUE, ntree = 50, depth = 20, nbins = 500\n", sep = ""))
      model = H2ORandomForestEstimator(
          ntrees=50, max_depth=20, nbins=500
      )
      model.train(x=["x1", "x2"], y="y", training_frame=train)
  
      # Test dataset has all 50 categories cat1 through cat50
      # Log.info("Importing 50_cattest_test.csv data...\n")
      test = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/50_cattest_test.csv"
          )
      )
  
      # Log.info("Summary of 50_cattest_test.csv from H2O:\n")
      # test.summary()
  
      # Predict on test dataset with DRF model:
      # Log.info("Performing predictions on test dataset...\n")
      preds = model.predict(test)
      preds.head()
  
      # Get the confusion matrix and AUC
      # Log.info("Confusion matrix of predictions (max accuracy):\n")
      perf = model.model_performance(test)
      perf.show()
      cm = perf.confusion_matrix()
      print(cm)
  
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: fiftycatRF() without return types