xgboost_classifier
===============================================================================

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

.. autoapi-nested-parse::

   XGBoost Classifier.



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

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

.. autoapisummary::

   evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: XGBoostClassifier(eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_seed=0, eval_metric='logloss', n_jobs=12, **kwargs)



   XGBoost Classifier.

   :param eta: Boosting learning rate. Defaults to 0.1.
   :type eta: float
   :param max_depth: Maximum tree depth for base learners. Defaults to 6.
   :type max_depth: int
   :param min_child_weight: Minimum sum of instance weight (hessian) needed in a child. Defaults to 1.0
   :type min_child_weight: float
   :param n_estimators: Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100.
   :type n_estimators: int
   :param random_seed: Seed for the random number generator. Defaults to 0.
   :type random_seed: int
   :param n_jobs: Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to 12.
   :type n_jobs: int


   **Attributes**

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

      * - **hyperparameter_ranges**
        - {    "eta": Real(0.000001, 1),    "max_depth": Integer(1, 10),    "min_child_weight": Real(1, 10),    "n_estimators": Integer(1, 1000),}
      * - **model_family**
        - ModelFamily.XGBOOST
      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **name**
        - XGBoost Classifier
      * - **SEED_MAX**
        - None
      * - **SEED_MIN**
        - None
      * - **supported_problem_types**
        - [    ProblemTypes.BINARY,    ProblemTypes.MULTICLASS,    ProblemTypes.TIME_SERIES_BINARY,    ProblemTypes.TIME_SERIES_MULTICLASS,]
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.clone
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.default_parameters
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.describe
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.feature_importance
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.fit
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.load
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.needs_fitting
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.parameters
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.predict
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.predict_proba
      evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.save

   .. 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:

      Feature importance of fitted XGBoost classifier.


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

      Fits XGBoost 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


   .. 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 fitted XGBoost classifier.

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

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


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

      Make predictions using the fitted CatBoost classifier.

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

      :returns: Predicted 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