class_docstr: Base functionality for some train-test performance checks.
docstr: Add condition - metric scores are greater than the threshold.
  
  Parameters
  ----------
  min_score : float
      Minimum score to pass the check.
docstr_tok: ["Add","condition","-","metric","scores","are","greater","than","the","threshold",".","Parameters","--","--","--","--","--","min_score",":","float","Minimum","score","to","pass","the","check","."]
code: def add_condition_test_performance_greater_than(
      self: Self, min_score: float
  ) -> Self:
      """Add condition - metric scores are greater than the threshold.
  
      Parameters
      ----------
      min_score : float
          Minimum score to pass the check.
      """
      condition = get_condition_test_performance_greater_than(
          min_score=min_score
      )
      return self.add_condition(
          f"Scores are greater than {min_score}", condition
      )
  
file_path: deepchecks/deepchecks/utils/abstracts/train_test_performace.py
imports: import abc
  import typing
  import pandas
  import plotly.express
  from typing_extensions import Self
  from deepchecks.core.check_utils.class_performance_utils import get_condition_class_performance_imbalance_ratio_less_than, get_condition_test_performance_greater_than, get_condition_train_test_relative_degradation_less_than
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES, colors
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 2 for calling the TrainTestPerformanceAbstract obj's underlying member method code with example usage: obj.add_condition_test_performance_greater_than(min_score) and returns: self