

.. _sphx_glr_auto_examples_ensemble:

.. _ensemble_examples:

Example using ensemble class methods
====================================

Under-sampling methods implies that samples of the majority class are lost during the balancing procedure.
Ensemble methods offer an alternative to use most of the samples.
In fact, an ensemble of balanced sets is created and used to later train any classifier.



.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how BalancedBaggingClassifier can be used to create a large variety of classifiers by giving different samplers.">

.. only:: html

  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_bagging_classifier_thumb.png
    :alt:

  :doc:`/auto_examples/ensemble/plot_bagging_classifier`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bagging classifiers using sampler</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Ensemble classifiers have shown to improve classification performance compare to single learner. However, they will be affected by class imbalance. This example shows the benefit of balancing the training set before to learn learners. We are making the comparison with non-balanced ensemble methods.">

.. only:: html

  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_comparison_ensemble_classifier_thumb.png
    :alt:

  :doc:`/auto_examples/ensemble/plot_comparison_ensemble_classifier`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Compare ensemble classifiers using resampling</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_bagging_classifier
   /auto_examples/ensemble/plot_comparison_ensemble_classifier

