plot_best_model
===============

.. py:module:: plot_best_model

.. autoapi-nested-parse::

   Module for analyzing and visualizing the single best performing model for each outcome.



Attributes
----------

.. autoapisummary::

   plot_best_model.MAX_OUTCOMES_TO_PLOT


Classes
-------

.. autoapisummary::

   plot_best_model.BestModelAnalyzerPlotter


Module Contents
---------------

.. py:data:: MAX_OUTCOMES_TO_PLOT
   :value: 10


.. py:class:: BestModelAnalyzerPlotter(data: pandas.DataFrame)

   Initialize the plotter.

   :param data: Aggregated results DataFrame. Must contain 'outcome_variable'.


   .. py:attribute:: data


   .. py:attribute:: clean_data


   .. py:attribute:: feature_categories
      :value: ['age', 'sex', 'bmi', 'ethnicity', 'bloods', 'diagnostic_order', 'drug_order', 'annotation_n',...



   .. py:attribute:: pipeline_params
      :value: ['resample', 'scale', 'param_space_size', 'percent_missing']



   .. py:method:: plot_best_model_summary(metric: str = 'auc', outcomes_to_plot: Optional[List[str]] = None, figsize: Tuple[int, int] = (14, 9))

      Generates a summary plot for the best model of each outcome.

      This method finds the best performing model for each outcome and creates
      a detailed 2x2 plot summarizing its algorithm, performance,
      hyperparameters, and pipeline settings.

      :param metric: The metric to determine the "best" model.
                     Defaults to 'auc'.
      :type metric: str, optional
      :param outcomes_to_plot: A specific list of
                               outcomes to analyze. If None, analyzes all outcomes up to a limit.
                               Defaults to None.
      :type outcomes_to_plot: Optional[List[str]], optional
      :param figsize: The figure size for each summary
                      plot. Defaults to (14, 9).
      :type figsize: Tuple[int, int], optional