.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/explainerdashboard/plot_main_01.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_explainerdashboard_plot_main_01.py>`
        to download the full example code

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

.. _sphx_glr__examples_explainerdashboard_plot_main_01.py:


01. Explainer Dash
------------------

This example runs an app which an interactive
user interface to explore the importance of
the features using SHAP values.

.. note:: https://github.com/oegedijk/explainerdashboard

.. GENERATED FROM PYTHON SOURCE LINES 12-35




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

 Out:

 .. code-block:: none


    "\n# Libraries\nfrom sklearn.ensemble import RandomForestClassifier\nfrom explainerdashboard import ClassifierExplainer\nfrom explainerdashboard import ExplainerDashboard\nfrom explainerdashboard.datasets import titanic_survive\nfrom explainerdashboard.datasets import feature_descriptions\n\n# Get data\nX_train, y_train, X_test, y_test = titanic_survive()\n\n# Create and fit model\nmodel = RandomForestClassifier(n_estimators=50, max_depth=10)     .fit(X_train, y_train)\n\n# Configure explainer\nexplainer = ClassifierExplainer(model, X_test, y_test,\n       cats=['Sex', 'Deck', 'Embarked'],\n       descriptions=feature_descriptions,\n       labels=['Not survived', 'Survived'])\n\n# Run\n#ExplainerDashboard(explainer).run()\n"





|

.. code-block:: default
   :lineno-start: 12

    """
    # Libraries
    from sklearn.ensemble import RandomForestClassifier
    from explainerdashboard import ClassifierExplainer
    from explainerdashboard import ExplainerDashboard
    from explainerdashboard.datasets import titanic_survive
    from explainerdashboard.datasets import feature_descriptions

    # Get data
    X_train, y_train, X_test, y_test = titanic_survive()

    # Create and fit model
    model = RandomForestClassifier(n_estimators=50, max_depth=10) \
        .fit(X_train, y_train)

    # Configure explainer
    explainer = ClassifierExplainer(model, X_test, y_test,
           cats=['Sex', 'Deck', 'Embarked'],
           descriptions=feature_descriptions,
           labels=['Not survived', 'Survived'])

    # Run
    #ExplainerDashboard(explainer).run()
    """

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

   **Total running time of the script:** ( 0 minutes  0.004 seconds)


.. _sphx_glr_download__examples_explainerdashboard_plot_main_01.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_main_01.py <plot_main_01.py>`



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

     :download:`Download Jupyter notebook: plot_main_01.ipynb <plot_main_01.ipynb>`


.. only:: html

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

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