class_docstr: Evaluation result for one metric.
  Stores all ExecutionCases with specified metric scores
  Computes human-friendly tables with results and some plots
class_docstr_tok: ["Evaluation","result","for","one","metric",".","Stores","all","ExecutionCases","with","specified","metric","scores","Computes","human-friendly","tables","with","results","and","some","plots"]
code: def _change_score_config(self, config):
      if config is not None:
          if isinstance(config, ScoreType):
              if config == ScoreType.Abs:
                  config = ScoreConfig.abs_score()
              elif config == ScoreType.Rel:
                  config = ScoreConfig.rel_score()
              else:
                  raise CatBoostError(
                      "Unknown scoreType {}".format(config)
                  )
          if self._score_config != config:
              self._score_config = config
              self.__clear_comparisons()
  
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 3 for calling the MetricEvaluationResult obj's underlying member method code with example usage: obj._change_score_config(config) without return types