sharkpy.battle
==============

.. py:module:: sharkpy.battle


Attributes
----------

.. autoapisummary::

   sharkpy.battle.MODEL_DETAILS


Functions
---------

.. autoapisummary::

   sharkpy.battle.battle
   sharkpy.battle._visualize_battle_results


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

.. py:data:: MODEL_DETAILS

.. py:function:: battle(self, data: pandas.DataFrame, target: str, models: List[str] = ['linear_regression', 'random_forest', 'xgboost'], metric: str = 'r2', n_trials: int = 30, early_stopping: bool = False, min_score: float = 0.5, verbose: int = 0) -> Dict

   Battle multiple models against each other and return the champion.

   :param data: Input data for training
   :type data: pd.DataFrame
   :param target: Name of target column
   :type target: str
   :param models: List of model names to compete
   :type models: list
   :param metric: Metric to compare models (default: 'r2')
   :type metric: str
   :param n_trials: Number of optimization trials for boosting models (default: 30)
   :type n_trials: int
   :param early_stopping: If True, stops training if any model exceeds `min_score`. Not recommended as it may miss better models later (default: False)
   :type early_stopping: bool, optional
   :param min_score: Minimum score to trigger early stopping (ignored if early_stopping=False) (default: 0.5)
   :type min_score: float
   :param verbose: Verbosity level for model training (default: 0)
   :type verbose: int

   :returns: Dictionary containing champion model name, model object, score, all results, details, and comparison plot
   :rtype: dict


.. py:function:: _visualize_battle_results(results)

   Create a bar plot of model performances.