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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_plot_diamonds.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_plot_diamonds.py:


Diamonds Dataset Visualization
==========================================
Regression on the classical diamond dataset.

.. GENERATED FROM PYTHON SOURCE LINES 6-15



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


    *

      .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_diamonds_001.png
         :alt: Target distribution
         :srcset: /auto_examples/plot/images/sphx_glr_plot_diamonds_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_diamonds_002.png
         :alt: Continuous Feature vs Target, F=9.63E-01, F=1.72E-01, F=1.00E-02
         :srcset: /auto_examples/plot/images/sphx_glr_plot_diamonds_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_diamonds_003.png
         :alt: Categorical Feature vs Target, F=3.61E-01, F=2.84E-01, F=1.02E-01
         :srcset: /auto_examples/plot/images/sphx_glr_plot_diamonds_003.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    /home/circleci/project/~/miniconda/envs/testenv/lib/python3.10/site-packages/sklearn/datasets/_openml.py:292: UserWarning: Multiple active versions of the dataset matching the name diamonds exist. Versions may be fundamentally different, returning version 1.
      warn(
    /home/circleci/project/~/miniconda/envs/testenv/lib/python3.10/site-packages/sklearn/datasets/_openml.py:932: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.
      warn(
    Target looks like regression
    /home/circleci/project/dabl/plot/supervised.py:107: UserWarning: Not plotting highly correlated (0.9961166041570525) feature carat. Set prune_correlations_threshold=0 to keep.
      warn(f"Not plotting highly correlated ({corr.max()})"
    /home/circleci/project/dabl/plot/supervised.py:107: UserWarning: Not plotting highly correlated (0.9978949275849379) feature y. Set prune_correlations_threshold=0 to keep.
      warn(f"Not plotting highly correlated ({corr.max()})"
    /home/circleci/project/dabl/plot/supervised.py:107: UserWarning: Not plotting highly correlated (0.9873553172140505) feature z. Set prune_correlations_threshold=0 to keep.
      warn(f"Not plotting highly correlated ({corr.max()})"






|

.. code-block:: default

    # sphinx_gallery_thumbnail_number = 2
    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_openml
    from dabl import plot

    X, y = fetch_openml('diamonds', 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  11.616 seconds)


.. _sphx_glr_download_auto_examples_plot_plot_diamonds.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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