ml_grid.model_classes.H2OBaseClassifier
=======================================

.. py:module:: ml_grid.model_classes.H2OBaseClassifier


Attributes
----------

.. autoapisummary::

   ml_grid.model_classes.H2OBaseClassifier.logger


Classes
-------

.. autoapisummary::

   ml_grid.model_classes.H2OBaseClassifier.H2OBaseClassifier


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

.. py:data:: logger

.. py:class:: H2OBaseClassifier(estimator_class=None, **kwargs)

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


   Initializes the H2OBaseClassifier.

   :param estimator_class: The H2O estimator class to be
                           wrapped (e.g., `H2OGradientBoostingEstimator`).
   :type estimator_class: Optional[type]
   :param \*\*kwargs: Additional keyword arguments to be passed to the H2O
                      estimator during initialization.


   .. py:attribute:: MIN_SAMPLES_FOR_STABLE_FIT
      :value: 10



   .. py:attribute:: estimator_class


   .. py:attribute:: logger


   .. py:attribute:: model_
      :type:  Optional[Any]
      :value: None



   .. py:attribute:: classes_
      :type:  Optional[numpy.ndarray]
      :value: None



   .. py:attribute:: feature_names_
      :type:  Optional[list]
      :value: None



   .. py:attribute:: feature_types_
      :type:  Optional[Dict[str, str]]
      :value: None



   .. py:method:: fit(X: pandas.DataFrame, y: pandas.Series, **kwargs) -> H2OBaseClassifier

      Fits the H2O model.

      :param X: The feature matrix.
      :type X: pd.DataFrame
      :param y: The target vector.
      :type y: pd.Series
      :param \*\*kwargs: Additional keyword arguments (not used).

      :returns: The fitted classifier instance.
      :rtype: H2OBaseClassifier



   .. py:method:: predict(X: pandas.DataFrame) -> numpy.ndarray

      Predicts class labels for samples in X.

      :param X: The feature matrix for prediction.
      :type X: pd.DataFrame

      :returns: An array of predicted class labels.
      :rtype: np.ndarray

      :raises RuntimeError: If the model is not fitted or if prediction fails.



   .. py:method:: predict_proba(X: pandas.DataFrame) -> numpy.ndarray

      Predicts class probabilities for samples in X.

      :param X: The feature matrix for prediction.
      :type X: pd.DataFrame

      :returns: An array of shape (n_samples, n_classes) with class probabilities.
      :rtype: np.ndarray

      :raises RuntimeError: If the model is not fitted or if prediction fails.



   .. py:method:: set_params(**kwargs: Any) -> H2OBaseClassifier

      Sets the parameters of this estimator, compatible with scikit-learn.

      :param \*\*kwargs: Keyword arguments representing the parameters to set.

      :returns: The classifier instance with updated parameters.
      :rtype: H2OBaseClassifier