class_docstr: Evaluator for multiclass classification.
  
  .. versionadded:: 1.4.0
  
  Parameters
  ----------
  predictionAndLabels : :py:class:`pyspark.RDD`
      an RDD of prediction, label, optional weight and optional probability.
  
  Examples
  --------
  >>> predictionAndLabels = sc.parallelize([(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
  ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)])
  >>> metrics = MulticlassMetrics(predictionAndLabels)
  >>> metrics.confusionMatrix().toArray()
  array([[ 2.,  1.,  1.],
         [ 1.,  3.,  0.],
         [ 0.,  0.,  1.]])
  >>> metrics.falsePositiveRate(0.0)
  0.2...
  >>> metrics.precision(1.0)
  0.75...
  >>> metrics.recall(2.0)
  1.0...
  >>> metrics.fMeasure(0.0, 2.0)
  0.52...
  >>> metrics.accuracy
  0.66...
  >>> metrics.weightedFalsePositiveRate
  0.19...
  >>> metrics.weightedPrecision
  0.68...
  >>> metrics.weightedRecall
  0.66...
  >>> metrics.weightedFMeasure()
  0.66...
  >>> metrics.weightedFMeasure(2.0)
  0.65...
  >>> predAndLabelsWithOptWeight = sc.parallelize([(0.0, 0.0, 1.0), (0.0, 1.0, 1.0),
  ...      (0.0, 0.0, 1.0), (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0),
  ...      (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)])
  >>> metrics = MulticlassMetrics(predAndLabelsWithOptWeight)
  >>> metrics.confusionMatrix().toArray()
  array([[ 2.,  1.,  1.],
         [ 1.,  3.,  0.],
         [ 0.,  0.,  1.]])
  >>> metrics.falsePositiveRate(0.0)
  0.2...
  >>> metrics.precision(1.0)
  0.75...
  >>> metrics.recall(2.0)
  1.0...
  >>> metrics.fMeasure(0.0, 2.0)
  0.52...
  >>> metrics.accuracy
  0.66...
  >>> metrics.weightedFalsePositiveRate
  0.19...
  >>> metrics.weightedPrecision
  0.68...
  >>> metrics.weightedRecall
  0.66...
  >>> metrics.weightedFMeasure()
  0.66...
  >>> metrics.weightedFMeasure(2.0)
  0.65...
  >>> predictionAndLabelsWithProbabilities = sc.parallelize([
  ...      (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]),
  ...      (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])])
  >>> metrics = MulticlassMetrics(predictionAndLabelsWithProbabilities)
  >>> metrics.logLoss()
  0.9682...
docstr: Returns weighted false positive rate.
docstr_tok: ["Returns","weighted","false","positive","rate","."]
code: def weightedFalsePositiveRate(self) -> float:
      """
      Returns weighted false positive rate.
      """
      return self.call("weightedFalsePositiveRate")
  
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 10 for calling the MulticlassMetrics obj's underlying member method code with example usage: obj.weightedFalsePositiveRate() and returns: self