code: def evaluate_model(
      model,
      feature: Optional[str] = None,
      fit_kwargs: Optional[dict] = None,
  ):
      """
      This function displays a user interface for analyzing performance of a trained
      model. It calls the ``plot_model`` function internally.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> anomaly = get_data('anomaly')
      >>> from pycaret.anomaly import *
      >>> exp_name = setup(data = anomaly)
      >>> knn = create_model('knn')
      >>> evaluate_model(knn)
  
  
      model: scikit-learn compatible object
          Trained model object
  
  
      feature: str, default = None
          Feature to be used as a hoverover tooltip and/or label when the ``label``
          param is set to True. When feature is None, first column of the dataset
          is used by default.
  
  
      fit_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the fit method of the model.
  
      Returns:
          None
  
  
      Warnings
      --------
      -   This function only works in IPython enabled Notebook.
  
      """
  
      return _CURRENT_EXPERIMENT.evaluate_model(
          estimator=model, feature_name=feature, fit_kwargs=fit_kwargs
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.anomaly.oop import AnomalyExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate_model(model, feature, fit_kwargs) and returns: _CURRENT_EXPERIMENT