ml_grid.model_classes.catboostClassifier
========================================

.. py:module:: ml_grid.model_classes.catboostClassifier


Classes
-------

.. autoapisummary::

   ml_grid.model_classes.catboostClassifier.CatBoostSKLearnWrapper


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

.. py:class:: CatBoostSKLearnWrapper(**kwargs: Any)

   Bases: :py:obj:`sklearn.base.BaseEstimator`, :py:obj:`sklearn.base.ClassifierMixin`


   Initializes the CatBoostSKLearnWrapper.

   :param \*\*kwargs: Keyword arguments passed directly to the
                      `catboost.CatBoostClassifier`.
   :type \*\*kwargs: Any


   .. py:attribute:: model


   .. py:method:: fit(X: Union[pandas.DataFrame, numpy.ndarray], y: Union[pandas.Series, numpy.ndarray]) -> CatBoostSKLearnWrapper

      Fits the CatBoost model.

      :param X: The training input samples.
      :type X: Union[pd.DataFrame, np.ndarray]
      :param y: The target values.
      :type y: Union[pd.Series, np.ndarray]

      :returns: The fitted estimator.
      :rtype: CatBoostSKLearnWrapper



   .. py:method:: predict(X: Union[pandas.DataFrame, numpy.ndarray]) -> numpy.ndarray

      Predicts class labels for samples in X.

      :param X: The input samples to predict.
      :type X: Union[pd.DataFrame, np.ndarray]

      :returns: The predicted class labels.
      :rtype: np.ndarray



   .. py:method:: predict_proba(X: Union[pandas.DataFrame, numpy.ndarray]) -> numpy.ndarray

      Predicts class probabilities for samples in X.

      :param X: The input samples.
      :type X: Union[pd.DataFrame, np.ndarray]

      :returns: The class probabilities of the input samples.
      :rtype: np.ndarray