code: def nb_iris():
      print("Importing iris_wheader.csv data...\n")
      iris = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      iris.describe()
  
      iris_nbayes = H2ONaiveBayesEstimator()
      iris_nbayes.train(
          x=list(range(4)),
          y=4,
          training_frame=iris,
          validation_frame=iris,
      )
      iris_nbayes.show()
  
      iris_nbayes = H2ONaiveBayesEstimator(nfolds=3, seed=1234)
      iris_nbayes.train(
          x=list(range(4)),
          y=4,
          training_frame=iris,
          validation_frame=iris,
      )
      iris_nbayes.show()
  
      iris_nbayes = H2ONaiveBayesEstimator(nfolds=3, seed=1234)
      iris_nbayes.train(x=list(range(4)), y=4, training_frame=iris)
      iris_nbayes.show()
  
      iris_nbayes = H2ONaiveBayesEstimator(
          nfolds=3, fold_assignment="Modulo"
      )
      iris_nbayes.train(x=list(range(4)), y=4, training_frame=iris)
      iris_nbayes.show()
  
      print("And here it is:")
      print(iris_nbayes.cross_validation_metrics_summary())
      print(
          iris_nbayes.cross_validation_metrics_summary().as_data_frame()
      )
      print(
          iris_nbayes.cross_validation_metrics_summary().as_data_frame()[
              "mean"
          ]
      )
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: nb_iris() without return types