class_docstr: Evaluator for multilabel classification.
  
  .. versionadded:: 1.4.0
  
  Parameters
  ----------
  predictionAndLabels : :py:class:`pyspark.RDD`
      an RDD of (predictions, labels) pairs,
      both are non-null Arrays, each with unique elements.
  
  Examples
  --------
  >>> predictionAndLabels = sc.parallelize([([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]),
  ...     ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]),
  ...     ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])])
  >>> metrics = MultilabelMetrics(predictionAndLabels)
  >>> metrics.precision(0.0)
  1.0
  >>> metrics.recall(1.0)
  0.66...
  >>> metrics.f1Measure(2.0)
  0.5
  >>> metrics.precision()
  0.66...
  >>> metrics.recall()
  0.64...
  >>> metrics.f1Measure()
  0.63...
  >>> metrics.microPrecision
  0.72...
  >>> metrics.microRecall
  0.66...
  >>> metrics.microF1Measure
  0.69...
  >>> metrics.hammingLoss
  0.33...
  >>> metrics.subsetAccuracy
  0.28...
  >>> metrics.accuracy
  0.54...
docstr: Returns recall or recall for a given label (category) if specified.
docstr_tok: ["Returns","recall","or","recall","for","a","given","label","(","category",")","if","specified","."]
code: def recall(self, label: Optional[float] = None) -> float:
      """
      Returns recall or recall for a given label (category) if specified.
      """
      if label is None:
          return self.call("recall")
      else:
          return self.call("recall", float(label))
  
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 MultilabelMetrics obj's underlying member method code with example usage: obj.recall(label) and returns: self, self