class_docstr: Summarize performance scores for multiple models on test datasets.
  
  Parameters
  ----------
  scorers: Union[Mapping[str, Union[str, Callable]], List[str]], default: None
      Scorers to override the default scorers, find more about the supported formats at
      https://docs.deepchecks.com/stable/user-guide/general/metrics_guide.html
  alternative_scorers : Dict[str, Callable] , default: None
      Deprecated, please use scorers instead.
  n_samples : int , default: 1_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
class_docstr_tok: ["Summarize","performance","scores","for","multiple","models","on","test","datasets",".","Parameters","--","--","--","--","--","scorers",":","Union","[","Mapping","[","str",",","Union","[","str",",","Callable","]","]",",","List","[","str","]","]",",","default",":","None","Scorers","to","override","the","default","scorers",",","find","more","about","the","supported","formats","at","https",":","\/\/docs.deepchecks.com\/stable\/user-guide\/general\/metrics_guide.html","alternative_scorers",":","Dict","[","str",",","Callable","]",",","default",":","None","Deprecated",",","please","use","scorers","instead",".","n_samples",":","int",",","default",":","1_000_000","number","of","samples","to","use","for","this","check",".","random_state",":","int",",","default",":","42","random","seed","for","all","check","internals","."]
code: def __init__(
      self,
      scorers: Union[
          Mapping[str, Union[str, Callable]], List[str]
      ] = None,
      alternative_scorers: Dict[str, Callable] = None,
      n_samples: int = 1_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if alternative_scorers is not None:
          warnings.warn(
              f"{self.__class__.__name__}: alternative_scorers is deprecated. Please use scorers instead.",
              DeprecationWarning,
          )
          self.scorers = alternative_scorers
      else:
          self.alternative_scorers = scorers
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/multi_model_performance_report.py
imports: import warnings
  from typing import TYPE_CHECKING, Callable, Dict, List, Mapping, Union, cast
  import pandas
  import plotly.express
  from deepchecks.core import CheckResult
  from deepchecks.tabular import ModelComparisonCheck, ModelComparisonContext
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.docref import doclink
label_desc: Use image node_id 1 to create a new MultiModelPerformanceReport object from inherited base classes: ModelComparisonCheck with example: obj = MultiModelPerformanceReport(scorers, alternative_scorers, n_samples, random_state)