class_docstr: A parent class to house common metrics available for the various Metrics types.
  
  The methods here are available across different model categories.
  
  .. note::
      This class and its subclasses are used at runtime as mixins: their methods can (and should) be accessed directly 
      from a metrics object, for example as a result of :func:`~h2o.model.ModelBase.model_performance`.
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docstr: The mean per class error.
  
  :examples:
  
  >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
  >>> prostate[2] = prostate[2].asfactor()
  >>> prostate[4] = prostate[4].asfactor()
  >>> prostate[5] = prostate[5].asfactor()
  >>> prostate[8] = prostate[8].asfactor()
  >>> predictors = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
  >>> response = "CAPSULE"
  >>> train, valid = prostate.split_frame(ratios=[.8],seed=1234)
  >>> pros_glm = H2OGeneralizedLinearEstimator(family="binomial")
  >>> pros_glm.train(x = predictors,
  ...                y = response,
  ...                training_frame = train,
  ...                validation_frame = valid)
  >>> pros_glm.mean_per_class_error()
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code: def mean_per_class_error(self):
      """The mean per class error.
  
      :examples:
  
      >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
      >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
      >>> prostate[2] = prostate[2].asfactor()
      >>> prostate[4] = prostate[4].asfactor()
      >>> prostate[5] = prostate[5].asfactor()
      >>> prostate[8] = prostate[8].asfactor()
      >>> predictors = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
      >>> response = "CAPSULE"
      >>> train, valid = prostate.split_frame(ratios=[.8],seed=1234)
      >>> pros_glm = H2OGeneralizedLinearEstimator(family="binomial")
      >>> pros_glm.train(x = predictors,
      ...                y = response,
      ...                training_frame = train,
      ...                validation_frame = valid)
      >>> pros_glm.mean_per_class_error()
      """
      return self._mean_per_class_error()
  
imports: from collections import OrderedDict
  from h2o.display import H2ODisplay, display, repr_def, format_to_html, format_to_multiline
  from h2o.utils.compatibility import *
  from h2o.utils.metaclass import backwards_compatibility, deprecated_fn, h2o_meta
  from h2o.utils.typechecks import is_type, numeric
label_desc: Use image node_id 31 for calling the MetricsBase obj's underlying member method code with example usage: obj.mean_per_class_error() and returns: self