class_docstr: Evaluator for Regression, which expects input columns prediction and label.
  Supported metrics are 'rmse', 'mse' and 'r2'.
  
  .. versionadded:: 3.5.0
  
  Examples
  --------
  >>> from pyspark.ml.connect.evaluation import RegressionEvaluator
  >>> eva = RegressionEvaluator(metricName='mse')
  >>> dataset = spark.createDataFrame(
  ...     [(1.0, 2.0), (-1.0, -1.5)], schema=['label', 'prediction']
  ... )
  >>> eva.evaluate(dataset)
  0.625
  >>> eva.isLargerBetter()
  False
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docstr: __init__(self, *, metricName='rmse', labelCol='label', predictionCol='prediction') -> None:
docstr_tok: ["__init__","(","self",",","*",",","metricName='rmse","'",",","labelCol='label","'",",","predictionCol='prediction","'",")","-",">","None",":"]
code: def __init__(
      self,
      *,
      metricName: str = "rmse",
      labelCol: str = "label",
      predictionCol: str = "prediction",
  ) -> None:
      """
      __init__(self, *, metricName='rmse', labelCol='label', predictionCol='prediction') -> None:
      """
      super().__init__()
      self._set(
          metricName=metricName,
          labelCol=labelCol,
          predictionCol=predictionCol,
      )
  
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 RegressionEvaluator object from inherited base classes: _TorchMetricEvaluator, HasLabelCol, HasPredictionCol, ParamsReadWrite with example: obj = RegressionEvaluator()