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
class_docstr_tok: ["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"]
code: def isLargerBetter(self) -> bool:
      if self.getOrDefault(self.metricName) == "r2":
          return True
  
      return False
  
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 5 for calling the RegressionEvaluator obj's underlying member method code with example usage: obj.isLargerBetter() and returns: False, True