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
  >>> juice = get_data('juice')
  >>> from pycaret.classification import *
  >>> exp_name = setup(data = juice,  target = 'Purchase')
  >>> lr = create_model('lr')
  >>> evaluate_model(lr)
  
  
  estimator: scikit-learn compatible object
      Trained model object
  
  
  fold: int or scikit-learn compatible CV generator, default = None
      Controls cross-validation. If None, the CV generator in the ``fold_strategy``
      parameter of the ``setup`` function is used. When an integer is passed,
      it is interpreted as the 'n_splits' parameter of the CV generator in the
      ``setup`` function.
  
  
  fit_kwargs: dict, default = {} (empty dict)
      Dictionary of arguments passed to the fit method of the model.
  
  
  plot_kwargs: dict, default = {} (empty dict)
      Dictionary of arguments passed to the visualizer class.
  
  
  groups: str or array-like, with shape (n_samples,), default = None
      Optional group labels when GroupKFold is used for the cross validation.
      It takes an array with shape (n_samples, ) where n_samples is the number
      of rows in training dataset. When string is passed, it is interpreted as
      the column name in the dataset containing group labels.
  
  
  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,
      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
      >>> juice = get_data('juice')
      >>> from pycaret.classification import *
      >>> exp_name = setup(data = juice,  target = 'Purchase')
      >>> lr = create_model('lr')
      >>> evaluate_model(lr)
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      fold: int or scikit-learn compatible CV generator, default = None
          Controls cross-validation. If None, the CV generator in the ``fold_strategy``
          parameter of the ``setup`` function is used. When an integer is passed,
          it is interpreted as the 'n_splits' parameter of the CV generator in the
          ``setup`` function.
  
  
      fit_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the fit method of the model.
  
  
      plot_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the visualizer class.
  
  
      groups: str or array-like, with shape (n_samples,), default = None
          Optional group labels when GroupKFold is used for the cross validation.
          It takes an array with shape (n_samples, ) where n_samples is the number
          of rows in training dataset. When string is passed, it is interpreted as
          the column name in the dataset containing group labels.
  
  
      Returns:
          None
  
  
      Warnings
      --------
      -   This function only works in IPython enabled Notebook.
  
      """
  
      return super().evaluate_model(
          estimator=estimator,
          fold=fold,
          fit_kwargs=fit_kwargs,
          plot_kwargs=plot_kwargs,
          groups=groups,
      )
  
imports: import datetime
  import gc
  import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from unittest.mock import patch
  import numpy
  import pandas
  import plotly.express
  import sklearn
  from joblib.memory import Memory
  from scipy.optimize import shgo
  from pycaret.containers.metrics.classification import get_all_metric_containers
  from pycaret.containers.models.classification import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.meta_estimators import CustomProbabilityThresholdClassifier, get_estimator_from_meta_estimator
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.internal.validation import is_sklearn_cv_generator
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, get_classification_task, get_label_encoder, highlight_setup
label_desc: Use image node_id 14 for calling the ClassificationExperiment obj's underlying member method code with example usage: obj.evaluate_model(estimator, fold, fit_kwargs, plot_kwargs, groups) and returns: super