.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_plot_mfeat_factors.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_plot_mfeat_factors.py:


mfeat-factors dataset visualization
==========================================
A multiclass dataset with 10 classes.
Linear discriminant analysis works surprisingly well!



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/plot/images/sphx_glr_plot_mfeat_factors_001.png
          :alt: Target distribution
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/plot/images/sphx_glr_plot_mfeat_factors_002.png
          :alt: F=1.06E+03, F=7.18E+02, F=7.16E+02, F=6.95E+02, F=6.64E+02, F=6.60E+02, F=6.58E+02, F=6.29E+02, F=6.12E+02, F=5.59E+02
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/plot/images/sphx_glr_plot_mfeat_factors_003.png
          :alt: Top feature interactions, 0.542, 0.537, 0.537, 0.535
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/plot/images/sphx_glr_plot_mfeat_factors_004.png
          :alt: Discriminating PCA directions, 0.563, 0.517, 0.512, Scree plot (PCA explained variance)
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/plot/images/sphx_glr_plot_mfeat_factors_005.png
          :alt: Discriminating LDA directions, 0.758, 0.740, 0.735, 0.728
          :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/circleci/miniconda/envs/testenv/lib/python3.8/site-packages/sklearn/datasets/_openml.py:373: UserWarning: Multiple active versions of the dataset matching the name mfeat-factors exist. Versions may be fundamentally different, returning version 1.
      warn("Multiple active versions of the dataset matching the name"
    Target looks like classification
    Showing only top 10 of 216 continuous features
    Linear Discriminant Analysis training set score: 0.993






|


.. code-block:: default

    # sphinx_gallery_thumbnail_number = 5
    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_openml
    from dabl import plot

    X, y = fetch_openml('mfeat-factors', as_frame=True, return_X_y=True)

    plot(X, y)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  10.001 seconds)


.. _sphx_glr_download_auto_examples_plot_plot_mfeat_factors.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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