.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "glr_examples/utils/plot_sbs_feature_selection.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_glr_examples_utils_plot_sbs_feature_selection.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_glr_examples_utils_plot_sbs_feature_selection.py:


=================================================
Plot feature selection with SBS 
=================================================

selects features using the sequential Backward Selection (SBS) algorithm. 

.. GENERATED FROM PYTHON SOURCE LINES 8-11

.. code-block:: Python

    # Author: L.Kouadio 
    # Licence: BSD-3-clause 








.. GENERATED FROM PYTHON SOURCE LINES 12-13

* SBS in action with fitted data 

.. GENERATED FROM PYTHON SOURCE LINES 13-28

.. code-block:: Python


    import matplotlib.pyplot as plt 
    from watex.exlib.sklearn import KNeighborsClassifier , train_test_split
    from watex.datasets import fetch_data
    from watex.base import SequentialBackwardSelection
    from watex.utils.plotutils import plot_sbs_feature_selection
    plt.style.use ('classic')

    X, y = fetch_data('bagoue analysed') # data already standardized
    Xtrain, Xt, ytrain,  yt = train_test_split(X, y)
    knn = KNeighborsClassifier(n_neighbors=5)
    sbs= SequentialBackwardSelection (knn)
    sbs.fit(Xtrain, ytrain )
    plot_sbs_feature_selection(sbs) 




.. image-sg:: /glr_examples/utils/images/sphx_glr_plot_sbs_feature_selection_001.png
   :alt: plot sbs feature selection
   :srcset: /glr_examples/utils/images/sphx_glr_plot_sbs_feature_selection_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 29-30

* Plot estimator with no prefit SBS  

.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. code-block:: Python


    plot_sbs_feature_selection(knn, Xtrain, ytrain) # yield the same result

    # The above pplot indicates that performance is mostly achieved from 
    # feature 3 to 4 before droppint around 60% with feature equals to 8 


.. image-sg:: /glr_examples/utils/images/sphx_glr_plot_sbs_feature_selection_002.png
   :alt: plot sbs feature selection
   :srcset: /glr_examples/utils/images/sphx_glr_plot_sbs_feature_selection_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.559 seconds)


.. _sphx_glr_download_glr_examples_utils_plot_sbs_feature_selection.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/watex/watex/0.3.X?urlpath=lab/tree/notebooks/glr_examples/utils/plot_sbs_feature_selection.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_sbs_feature_selection.ipynb <plot_sbs_feature_selection.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_sbs_feature_selection.py <plot_sbs_feature_selection.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_