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


=================================================
Plot learning curves  
=================================================

plots inline multiple models learning curves. 

.. GENERATED FROM PYTHON SOURCE LINES 8-11

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 12-13

* Plot metaestimator already cross-validated. 

.. GENERATED FROM PYTHON SOURCE LINES 13-21

.. code-block:: Python


    from watex.models.premodels import p 
    from watex.datasets import fetch_data 
    from watex.utils.plotutils import plot_learning_curves
    X, y = fetch_data ('bagoue prepared') # yields a sparse matrix 
    # let collect 04 estimators already cross-validated from SVMs
    models = [ p.SVM.linear , p.SVM.rbf , p.SVM.sigmoid , p.SVM.poly ]
    plot_learning_curves (models, X, y, cv=4, sns_style = 'darkgrid')



.. image-sg:: /glr_examples/utils/images/sphx_glr_plot_learning_curves_001.png
   :alt: linear, rbf, sigmoid, poly
   :srcset: /glr_examples/utils/images/sphx_glr_plot_learning_curves_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 22-23

* Plot  multiples models not crossvalidated yet. It take a little bit times 

.. GENERATED FROM PYTHON SOURCE LINES 23-32

.. code-block:: Python


    from watex.exlib.sklearn import (
        LogisticRegression, 
        RandomForestClassifier, 
        SVC , KNeighborsClassifier 
        )
    models =[LogisticRegression(), RandomForestClassifier(), SVC() ,
                 KNeighborsClassifier() 
                 ]
    plot_learning_curves (models, X, y, cv=4, sns_style = 'darkgrid')


.. image-sg:: /glr_examples/utils/images/sphx_glr_plot_learning_curves_002.png
   :alt: LogisticRegression, RandomForestClassifier, SVC, KNeighborsClassifier
   :srcset: /glr_examples/utils/images/sphx_glr_plot_learning_curves_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 51.174 seconds)


.. _sphx_glr_download_glr_examples_utils_plot_learning_curves.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_learning_curves.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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