ml_grid.model_classes.AutoGluonClassifier
=========================================

.. py:module:: ml_grid.model_classes.AutoGluonClassifier

.. autoapi-nested-parse::

   AutoGluon Classifier Wrapper.

   This module provides a scikit-learn compatible wrapper for AutoGluon's TabularPredictor.



Attributes
----------

.. autoapisummary::

   ml_grid.model_classes.AutoGluonClassifier.TabularPredictor
   ml_grid.model_classes.AutoGluonClassifier.logger


Classes
-------

.. autoapisummary::

   ml_grid.model_classes.AutoGluonClassifier.AutoGluonClassifier


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

.. py:data:: TabularPredictor
   :value: None


.. py:data:: logger

.. py:class:: AutoGluonClassifier(time_limit: int = 120, presets: Optional[str] = None, eval_metric: str = 'accuracy', problem_type: Optional[str] = None, seed: int = 42, verbosity: int = 2, path: Optional[str] = None, excluded_model_types: Optional[List[str]] = None, hyperparameters: Optional[dict] = None)

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


   A scikit-learn compatible wrapper for AutoGluon TabularPredictor.


   .. py:attribute:: time_limit
      :value: 120



   .. py:attribute:: presets
      :value: None



   .. py:attribute:: eval_metric
      :value: 'accuracy'



   .. py:attribute:: problem_type
      :value: None



   .. py:attribute:: seed
      :value: 42



   .. py:attribute:: verbosity
      :value: 2



   .. py:attribute:: path
      :value: None



   .. py:attribute:: excluded_model_types
      :value: None



   .. py:attribute:: hyperparameters
      :value: None



   .. py:attribute:: predictor_
      :value: None



   .. py:attribute:: classes_
      :value: None



   .. py:attribute:: model_id
      :value: None



   .. py:attribute:: timed_out_
      :value: False



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


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


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