sharkpy.battle

Attributes

MODEL_DETAILS

Functions

battle(→ Dict)	Battle multiple models against each other and return the champion.
_visualize_battle_results(results)	Create a bar plot of model performances.

Module Contents

sharkpy.battle.MODEL_DETAILS

sharkpy.battle.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.

Parameters:

• data (pd.DataFrame) – Input data for training

• target (str) – Name of target column

• models (list) – List of model names to compete

• metric (str) – Metric to compare models (default: ‘r2’)

• n_trials (int) – Number of optimization trials for boosting models (default: 30)

• early_stopping (bool, optional) – If True, stops training if any model exceeds min_score. Not recommended as it may miss better models later (default: False)

• min_score (float) – Minimum score to trigger early stopping (ignored if early_stopping=False) (default: 0.5)

• verbose (int) – Verbosity level for model training (default: 0)

Returns:

Dictionary containing champion model name, model object, score, all results, details, and comparison plot

Return type:

dict

sharkpy.battle._visualize_battle_results(results)

Create a bar plot of model performances.