.. _sphx_glr_auto_examples_03_model_selection:

.. _model_selection:

===============
Model Selection
===============

This section contains how one could use sklearn's `crossvalidation`_ and `gridsearch`_ methods in
combination with the models provided in the sklvq package.

.. _crossvalidation: https://scikit-learn.org/stable/modules/cross_validation.html
.. _gridsearch: https://scikit-learn.org/stable/modules/grid_search.html


.. raw:: html

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

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In all previous examples we showed the training performance of the models. However, in practice it is much more interesting how well a model performs on unseen data, i.e., the generalizability of the model. We can use `crossvalidation`_ for this.">

.. only:: html

  .. image:: /auto_examples/03_model_selection/images/thumb/sphx_glr_plot_01_crossvalidation_lvq_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_model_selection_plot_01_crossvalidation_lvq.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Cross validation</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Cross validation  is not the whole story as it only can tell you  the expected performance of a single set of (hyper) parameters. Luckily  sklearn also provides a way of trying out multiple settings and return the CV scores for each of them. We can use `gridsearch`_ for this.">

.. only:: html

  .. image:: /auto_examples/03_model_selection/images/thumb/sphx_glr_plot_02_gridsearch_lvq_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_model_selection_plot_02_gridsearch_lvq.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Grid Search</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/03_model_selection/plot_01_crossvalidation_lvq
   /auto_examples/03_model_selection/plot_02_gridsearch_lvq