baseline_classifier
================================================================================

.. py:module:: evalml.pipelines.components.estimators.classifiers.baseline_classifier

.. autoapi-nested-parse::

   Baseline classifier.



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

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: BaselineClassifier(strategy='mode', random_seed=0, **kwargs)



   Classifier that predicts using the specified strategy.

   This is useful as a simple baseline classifier to compare with other classifiers.

   :param strategy: Method used to predict. Valid options are "mode", "random" and "random_weighted". Defaults to "mode".
   :type strategy: str
   :param random_seed: Seed for the random number generator. Defaults to 0.
   :type random_seed: int


   **Attributes**

   .. list-table::
      :widths: 15 85
      :header-rows: 0

      * - **hyperparameter_ranges**
        - {}
      * - **model_family**
        - ModelFamily.BASELINE
      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **name**
        - Baseline Classifier
      * - **supported_problem_types**
        - [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.classes_
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.clone
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.default_parameters
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.describe
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.feature_importance
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.fit
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.load
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.needs_fitting
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.parameters
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.predict
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.predict_proba
      evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.save

   .. py:method:: classes_(self)
      :property:

      Returns class labels. Will return None before fitting.

      :returns: Class names
      :rtype: list[str] or list(float)


   .. py:method:: clone(self)

      Constructs a new component with the same parameters and random state.

      :returns: A new instance of this component with identical parameters and random state.


   .. py:method:: default_parameters(cls)

      Returns the default parameters for this component.

      Our convention is that Component.default_parameters == Component().parameters.

      :returns: Default parameters for this component.
      :rtype: dict


   .. py:method:: describe(self, print_name=False, return_dict=False)

      Describe a component and its parameters.

      :param print_name: whether to print name of component
      :type print_name: bool, optional
      :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters}
      :type return_dict: bool, optional

      :returns: Returns dictionary if return_dict is True, else None.
      :rtype: None or dict


   .. py:method:: feature_importance(self)
      :property:

      Returns importance associated with each feature. Since baseline classifiers do not use input features to calculate predictions, returns an array of zeroes.

      :returns: An array of zeroes
      :rtype: pd.Series


   .. py:method:: fit(self, X, y=None)

      Fits baseline classifier component to data.

      :param X: The input training data of shape [n_samples, n_features].
      :type X: pd.DataFrame
      :param y: The target training data of length [n_samples].
      :type y: pd.Series

      :returns: self

      :raises ValueError: If y is None.


   .. py:method:: load(file_path)
      :staticmethod:

      Loads component at file path.

      :param file_path: Location to load file.
      :type file_path: str

      :returns: ComponentBase object


   .. py:method:: needs_fitting(self)

      Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

      This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

      :returns: True.


   .. py:method:: parameters(self)
      :property:

      Returns the parameters which were used to initialize the component.


   .. py:method:: predict(self, X)

      Make predictions using the baseline classification strategy.

      :param X: Data of shape [n_samples, n_features].
      :type X: pd.DataFrame

      :returns: Predicted values.
      :rtype: pd.Series


   .. py:method:: predict_proba(self, X)

      Make prediction probabilities using the baseline classification strategy.

      :param X: Data of shape [n_samples, n_features].
      :type X: pd.DataFrame

      :returns: Predicted probability values.
      :rtype: pd.DataFrame


   .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

      Saves component at file path.

      :param file_path: Location to save file.
      :type file_path: str
      :param pickle_protocol: The pickle data stream format.
      :type pickle_protocol: int