class_docstr: Config to present human-friendly evaluation results.
class_docstr_tok: ["Config","to","present","human-friendly","evaluation","results","."]
docstr: :param score_type: type of score. For abs difference score will be (baseline - test).mean(),
  for relative it's ((baseline - test) / baseline).mean()
  :param multiplier: multiplier to print score
  :param score_level: WX-test level. Will be used to make if tested case significantly better or worse
  :param interval_level: level to compute score confidence interval
  :param overfit_iterations_info: if information about overfit iterations should be preserved
docstr_tok: [":","param","score_type",":","type","of","score",".","For","abs","difference","score","will","be","(","baseline","-","test",")",".mean","(",")",",","for","relative","it","'s","(","(","baseline","-","test",")","\/","baseline",")",".mean","(",")",":","param","multiplier",":","multiplier","to","print","score",":","param","score_level",":","WX-test","level",".","Will","be","used","to","make","if","tested","case","significantly","better","or","worse",":","param","interval_level",":","level","to","compute","score","confidence","interval",":","param","overfit_iterations_info",":","if","information","about","overfit","iterations","should","be","preserved"]
code: def __init__(
      self,
      score_type=ScoreType.Rel,
      multiplier=100,
      score_level=0.01,
      interval_level=0.01,
      overfit_iterations_info=True,
  ):
      """
  
      :param score_type: type of score. For abs difference score will be (baseline - test).mean(),
      for relative it's ((baseline - test) / baseline).mean()
      :param multiplier: multiplier to print score
      :param score_level: WX-test level. Will be used to make if tested case significantly better or worse
      :param interval_level: level to compute score confidence interval
      :param overfit_iterations_info: if information about overfit iterations should be preserved
      """
      self.type = score_type
      self.multiplier = multiplier
      self.score_level = score_level
      self.interval_level = interval_level
      self.overfit_overfit_iterations_info = overfit_iterations_info
  
imports: import numpy
  import pandas
  from enum import Enum
  from ..None import CatBoostError
  from ..core import metric_description_or_str_to_str
  from ..utils import compute_wx_test
label_desc: Use image node_id 1 to create a new ScoreConfig object  with example: obj = ScoreConfig(score_type, multiplier, score_level, interval_level, overfit_iterations_info)