docstr: This function analyzes the performance of a trained model.
  
  
  Example
  -------
  >>> from pycaret.datasets import get_data
  >>> anomaly = get_data('anomaly')
  >>> from pycaret.anomaly import *
  >>> exp_name = setup(data = anomaly)
  >>> knn = create_model('knn')
  >>> plot_model(knn, plot = 'tsne')
  
  
  model: scikit-learn compatible object
      Trained Model Object
  
  
  plot: str, default = 'tsne'
      List of available plots (ID - Name):
  
      * 'tsne' - t-SNE (3d) Dimension Plot
      * 'umap' - UMAP Dimensionality Plot
  
  
  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.
  
  
  label: bool, default = False
      Name of column to be used as data labels.
  
  
  scale: float, default = 1
      The resolution scale of the figure.
  
  
  save: bool, default = False
      When set to True, plot is saved in the current working directory.
  
  
  display_format: str, default = None
      To display plots in Streamlit (https://www.streamlit.io/), set this to 'streamlit'.
      Currently, not all plots are supported.
  
  
  Returns:
      Path to saved file, if any.
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code: def plot_model(
      self,
      estimator,
      plot: str = "auc",
      scale: float = 1,
      save: Union[str, bool] = False,
      fold: Optional[Union[int, Any]] = None,
      fit_kwargs: Optional[dict] = None,
      plot_kwargs: Optional[dict] = None,
      groups: Optional[Union[str, Any]] = None,
      feature_name: Optional[str] = None,
      label: bool = False,
      verbose: bool = True,
      display_format: Optional[str] = None,
  ) -> Optional[str]:
      """
      This function analyzes the performance of a trained model.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> anomaly = get_data('anomaly')
      >>> from pycaret.anomaly import *
      >>> exp_name = setup(data = anomaly)
      >>> knn = create_model('knn')
      >>> plot_model(knn, plot = 'tsne')
  
  
      model: scikit-learn compatible object
          Trained Model Object
  
  
      plot: str, default = 'tsne'
          List of available plots (ID - Name):
  
          * 'tsne' - t-SNE (3d) Dimension Plot
          * 'umap' - UMAP Dimensionality Plot
  
  
      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.
  
  
      label: bool, default = False
          Name of column to be used as data labels.
  
  
      scale: float, default = 1
          The resolution scale of the figure.
  
  
      save: bool, default = False
          When set to True, plot is saved in the current working directory.
  
  
      display_format: str, default = None
          To display plots in Streamlit (https://www.streamlit.io/), set this to 'streamlit'.
          Currently, not all plots are supported.
  
  
      Returns:
          Path to saved file, if any.
  
      """
      return super().plot_model(
          estimator,
          plot,
          scale,
          save,
          fold,
          fit_kwargs,
          plot_kwargs,
          groups,
          feature_name,
          label,
          verbose,
          display_format,
      )
  
file_path: pycaret/pycaret/anomaly/oop.py
imports: from typing import Any, List, Optional, Tuple, Union
  import pandas
  from pycaret.containers.metrics.anomaly import get_all_metric_containers
  from pycaret.containers.models.anomaly import get_all_model_containers
  from pycaret.internal.logging import get_logger
  from pycaret.internal.pycaret_experiment.unsupervised_experiment import _UnsupervisedExperiment
  from pycaret.utils.generic import MLUsecase
label_desc: Use image node_id 6 for calling the AnomalyExperiment obj's underlying member method code with example usage: obj.plot_model(estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, verbose, display_format) and returns: super