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 __init__(self, case_results):
      if len(case_results) <= 1:
          raise CatBoostError(
              "Need at least 2 case results, got {} ".format(
                  len(case_results)
              )
          )
  
      self._case_results = dict()
      self._case_comparisons = dict()
      self._cases = [
          case_result.get_case() for case_result in case_results
      ]
  
      for case_result in case_results:
          case = case_result.get_case()
          self._case_results[case] = case_result
  
      self._metric_description = case_results[
          0
      ].get_metric_description()
      self._baseline_case = case_results[0].get_case()
  
      self._score_config = ScoreConfig()
  
      for case, case_result in self._case_results.items():
          if (
              case_result.get_metric_description()
              != self._metric_description
          ):
              raise CatBoostError(
                  "Metric names should be equal for all case results"
              )
  
          if case_result.get_fold_ids() != self.get_fold_ids():
              raise CatBoostError(
                  "Case results should be computed on the same folds"
              )
  
          if case_result.get_eval_step() != self.get_eval_step():
              raise CatBoostError(
                  "Eval steps should be equal for different cases"
              )
  
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 MetricEvaluationResult object  with example: obj = MetricEvaluationResult(case_results)