mds_2025_helper_functions.scores
================================

.. py:module:: mds_2025_helper_functions.scores


Functions
---------

.. autoapisummary::

   mds_2025_helper_functions.scores.compare_model_scores


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

.. py:function:: compare_model_scores(*args, X, y=None, scoring=None, return_train_scores=False, **kwargs)

   Creates a table comparing mean cross-validation scores of multiple models.

   :param \*args: Model objects implementing the `fit` method. At least two models are required.
   :type \*args: sklearn.base.BaseEstimator
   :param X: Training data.
   :type X: array-like of shape (n_samples, n_features)
   :param y: Target values for supervised learning tasks.
   :type y: array-like of shape (n_samples,) or (n_samples, n_outputs), optional
   :param scoring: Metrics to evaluate models. Refer to `scikit-learn` scoring documentation:
                   https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter.
   :type scoring: str, callable, list, tuple, or dict, optional
   :param return_train_scores: Whether to include training scores in addition to test scores.
   :type return_train_scores: bool, default=False
   :param \*\*kwargs: Additional arguments passed to `sklearn.model_selection.cross_validate`.
   :type \*\*kwargs: dict

   :returns: A DataFrame comparing model performance:
             - Rows represent different models.
             - Columns include metrics from cross-validation.
             - Index contains model names.
   :rtype: pd.DataFrame

   .. rubric:: Examples

   >>> from sklearn.linear_model import LogisticRegression
   >>> from sklearn.ensemble import RandomForestClassifier
   >>> compare_model_scores(LogisticRegression(), RandomForestClassifier(), X=X_train, y=y_train, scoring="accuracy")