class_docstr: Evaluator for binary classification.
  
  .. versionadded:: 1.4.0
  
  Parameters
  ----------
  scoreAndLabels : :py:class:`pyspark.RDD`
      an RDD of score, label and optional weight.
  
  Examples
  --------
  >>> scoreAndLabels = sc.parallelize([
  ...     (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2)
  >>> metrics = BinaryClassificationMetrics(scoreAndLabels)
  >>> metrics.areaUnderROC
  0.70...
  >>> metrics.areaUnderPR
  0.83...
  >>> metrics.unpersist()
  >>> scoreAndLabelsWithOptWeight = sc.parallelize([
  ...     (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0, 0.6), (0.6, 1.0, 0.9),
  ...     (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2)
  >>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight)
  >>> metrics.areaUnderROC
  0.79...
  >>> metrics.areaUnderPR
  0.88...
docstr: Computes the area under the precision-recall curve.
docstr_tok: ["Computes","the","area","under","the","precision-recall","curve","."]
code: def areaUnderPR(self) -> float:
      """
      Computes the area under the precision-recall curve.
      """
      return self.call("areaUnderPR")
  
imports: from typing import Generic, List, Optional, Tuple, TypeVar, Union
  import sys
  from pyspark import since
  from pyspark.rdd import RDD
  from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
  from pyspark.mllib.linalg import Matrix
  from pyspark.sql import SQLContext
  from pyspark.sql.types import ArrayType, DoubleType, StructField, StructType
label_desc: Use image node_id 3 for calling the BinaryClassificationMetrics obj's underlying member method code with example usage: obj.areaUnderPR() and returns: self