.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "glr_examples/base/plot_sbs_feature_selector.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_base_plot_sbs_feature_selector.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_base_plot_sbs_feature_selector.py:


============================================
Plot Sequential Backward Selection  (SBS)
============================================

shows the SBS in action with the classification 
accuracy of the KNeighbors Classifier that was 
calculated on the validation dataset. 

.. GENERATED FROM PYTHON SOURCE LINES 10-13

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 14-15

imort the required modules and preprocess the data 

.. GENERATED FROM PYTHON SOURCE LINES 15-43

.. code-block:: Python

    from watex.datasets import fetch_data 
    from watex.exlib.sklearn import SimpleImputer 
    from watex.utils import selectfeatures
    import matplotlib.pyplot as plt 
    from watex.exlib.sklearn import KNeighborsClassifier 
    from watex.base import SequentialBackwardSelection 

    data= fetch_data("bagoue original").get('data=dfy1') # encoded flow categories 
    y = data.flow ; X= data.drop(columns='flow') 
    # select the numerical features 
    X =selectfeatures(X, include ='number')
    # imputed the missing data 
    X = SimpleImputer().fit_transform(X)

    knn = KNeighborsClassifier(n_neighbors=5)
    sbs= SequentialBackwardSelection(knn, k_features=1) 
    sbs.fit(X, y)

    k_feat = [len(k) for k in sbs.subsets_]
    plt.plot (k_feat, sbs.scores_, marker ='o')
    #plt.ylim ([])
    plt.ylabel ('Accuracy')
    plt.xlabel ("Number of features")
    plt.grid () 
    plt.tight_layout()
    plt.show() 

    # AS we can see the classifier achieve more than 70% accuracy for 
    # k =3, 4, 5. Thus, we can reduce the number of features down to. 


.. image-sg:: /glr_examples/base/images/sphx_glr_plot_sbs_feature_selector_001.png
   :alt: plot sbs feature selector
   :srcset: /glr_examples/base/images/sphx_glr_plot_sbs_feature_selector_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.339 seconds)


.. _sphx_glr_download_glr_examples_base_plot_sbs_feature_selector.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/base/plot_sbs_feature_selector.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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