class_docstr: Evaluator for binary classification, which expects input columns prediction and label.
  Supported metrics are 'areaUnderROC' and 'areaUnderPR'.
  
  .. versionadded:: 3.5.0
  
  Examples
  --------
  >>> from pyspark.ml.connect.evaluation import BinaryClassificationEvaluator
  >>> eva = BinaryClassificationEvaluator(metricName='areaUnderPR')
  >>> dataset = spark.createDataFrame(
  ...     [(1, 0.6), (0, 0.55), (0, 0.1), (1, 0.6), (1, 0.4)],
  ...     schema=['label', 'probability']
  ... )
  >>> eva.evaluate(dataset)
  0.9166666865348816
  >>> eva.isLargerBetter()
  True
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docstr: __init__(
      self,
      *,
      metricName='rmse',
      labelCol='label',
      probabilityCol='probability'
  ) -> None:
docstr_tok: ["__init__","(","self",",","*",",","metricName='rmse","'",",","labelCol='label","'",",","probabilityCol='probability'",")","-",">","None",":"]
code: def __init__(
      self,
      *,
      metricName: str = "areaUnderROC",
      labelCol: str = "label",
      probabilityCol: str = "probability",
  ) -> None:
      """
      __init__(
          self,
          *,
          metricName='rmse',
          labelCol='label',
          probabilityCol='probability'
      ) -> None:
      """
      super().__init__()
      self._set(
          metricName=metricName,
          labelCol=labelCol,
          probabilityCol=probabilityCol,
      )
  
imports: from typing import Any, Union, List, Tuple
  import numpy
  import pandas
  from pyspark import keyword_only
  from pyspark.ml.param import Param, Params, TypeConverters
  from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasProbabilityCol
  from pyspark.ml.connect.base import Evaluator
  from pyspark.ml.connect.io_utils import ParamsReadWrite
  from pyspark.ml.connect.util import aggregate_dataframe
  from pyspark.sql import DataFrame
label_desc: Use image node_id 1 to create a new BinaryClassificationEvaluator object from inherited base classes: _TorchMetricEvaluator, HasLabelCol, HasProbabilityCol, ParamsReadWrite with example: obj = BinaryClassificationEvaluator()