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`.
class_docstr_tok: ["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","`","."]
docstr: The area under the precision recall curve.
  
  :examples:
  
  >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> response = "economy_20mpg"
  >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234)
  >>> cars_gbm = H2OGradientBoostingEstimator(seed = 1234) 
  >>> cars_gbm.train(x = predictors,
  ...                y = response,
  ...                training_frame = train,
  ...                validation_frame = valid)
  >>> cars_gbm.aucpr()
docstr_tok: ["The","area","under","the","precision","recall","curve",".",":","examples",":",">",">",">","from","h2o.estimators.gbm","import","H2OGradientBoostingEstimator",">",">",">","cars","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/junit\/cars_20mpg.csv","''",")",">",">",">","cars","[","``","economy_20mpg","''","]","=","cars","[","``","economy_20mpg","''","]",".asfactor","(",")",">",">",">","predictors","=","[","``","displacement","''",",","''","power","''",",","''","weight","''",",","''","acceleration","''",",","''","year","''","]",">",">",">","response","=","``","economy_20mpg","''",">",">",">","train",",","valid","=","cars.split_frame","(","ratios","=","[",".8","]",",","seed","=","1234",")",">",">",">","cars_gbm","=","H2OGradientBoostingEstimator","(","seed","=","1234",")",">",">",">","cars_gbm.train","(","x","=","predictors",",","...","y","=","response",",","...","training_frame","=","train",",","...","validation_frame","=","valid",")",">",">",">","cars_gbm.aucpr","(",")"]
code: def aucpr(self):
      """The area under the precision recall curve.
  
      :examples:
  
      >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> response = "economy_20mpg"
      >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234)
      >>> cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
      >>> cars_gbm.train(x = predictors,
      ...                y = response,
      ...                training_frame = train,
      ...                validation_frame = valid)
      >>> cars_gbm.aucpr()
      """
      return self._metric_json["pr_auc"]
  
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 16 for calling the MetricsBase obj's underlying member method code with example usage: obj.aucpr() and returns: self