class_docstr: Naive Bayes
  
  The naive Bayes classifier assumes independence between predictor variables
  conditional on the response, and a Gaussian distribution of numeric predictors with
  mean and standard deviation computed from the training dataset. When building a naive
  Bayes classifier, every row in the training dataset that contains at least one NA will
  be skipped completely. If the test dataset has missing values, then those predictors
  are omitted in the probability calculation during prediction.
class_docstr_tok: ["Naive","Bayes","The","naive","Bayes","classifier","assumes","independence","between","predictor","variables","conditional","on","the","response",",","and","a","Gaussian","distribution","of","numeric","predictors","with","mean","and","standard","deviation","computed","from","the","training","dataset",".","When","building","a","naive","Bayes","classifier",",","every","row","in","the","training","dataset","that","contains","at","least","one","NA","will","be","skipped","completely",".","If","the","test","dataset","has","missing","values",",","then","those","predictors","are","omitted","in","the","probability","calculation","during","prediction","."]
docstr: Balance training data class counts via over/under-sampling (for imbalanced data).
  
  Type: ``bool``, defaults to ``False``.
  
  :examples:
  
  >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
  >>> iris_nb = H2ONaiveBayesEstimator(balance_classes=False,
  ...                                  nfolds=3,
  ...                                  seed=1234)
  >>> iris_nb.train(x=list(range(4)),
  ...               y=4,
  ...               training_frame=iris)
  >>> iris_nb.mse()
code: def balance_classes(self):
      """
      Balance training data class counts via over/under-sampling (for imbalanced data).
  
      Type: ``bool``, defaults to ``False``.
  
      :examples:
  
      >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
      >>> iris_nb = H2ONaiveBayesEstimator(balance_classes=False,
      ...                                  nfolds=3,
      ...                                  seed=1234)
      >>> iris_nb.train(x=list(range(4)),
      ...               y=4,
      ...               training_frame=iris)
      >>> iris_nb.mse()
      """
      return self._parms.get("balance_classes")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 28 for calling the H2ONaiveBayesEstimator obj's underlying member method code with example usage: obj.balance_classes() and returns: self