docstr: 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
  >>> jewellery = get_data('jewellery')
  >>> from pycaret.clustering import *
  >>> exp_name = setup(data = jewellery)
  >>> kmeans = create_model('kmeans')
  >>> evaluate_model(kmeans)
  
  
  model: scikit-learn compatible object
      Trained model object
  
  
  feature: str, default = None
      Feature to be evaluated when plot = 'distribution'. When ``plot`` type is
      'cluster' or 'tsne' feature column is used as a hoverover tooltip and/or
      label when the ``label`` param is set to True. When the ``plot`` type is
      'cluster' or 'tsne' and feature is None, first column of the dataset is
      used.
  
  
  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.
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code: def evaluate_model(
      self,
      estimator,
      fold: Optional[Union[int, Any]] = None,
      fit_kwargs: Optional[dict] = None,
      plot_kwargs: Optional[dict] = None,
      feature_name: Optional[str] = None,
      groups: Optional[Union[str, Any]] = 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
      >>> jewellery = get_data('jewellery')
      >>> from pycaret.clustering import *
      >>> exp_name = setup(data = jewellery)
      >>> kmeans = create_model('kmeans')
      >>> evaluate_model(kmeans)
  
  
      model: scikit-learn compatible object
          Trained model object
  
  
      feature: str, default = None
          Feature to be evaluated when plot = 'distribution'. When ``plot`` type is
          'cluster' or 'tsne' feature column is used as a hoverover tooltip and/or
          label when the ``label`` param is set to True. When the ``plot`` type is
          'cluster' or 'tsne' and feature is None, first column of the dataset is
          used.
  
  
      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 super().evaluate_model(
          estimator,
          fold,
          fit_kwargs,
          plot_kwargs,
          feature_name,
          groups,
      )
  
imports: import datetime
  import gc
  import logging
  import time
  import traceback
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from sklearn.base import clone
  from pycaret.containers.metrics.clustering import get_all_metric_containers
  from pycaret.containers.models.clustering import ALL_ALLOWED_ENGINES, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger, redirect_output
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.pipeline import estimator_pipeline, get_pipeline_fit_kwargs
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.tabular_experiment import _TabularExperiment
  from pycaret.internal.validation import is_sklearn_pipeline
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 10 for calling the _UnsupervisedExperiment obj's underlying member method code with example usage: obj.evaluate_model(estimator, fold, fit_kwargs, plot_kwargs, feature_name, groups) and returns: super