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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_explain_example.py:


Model Explanation
=================

.. GENERATED FROM PYTHON SOURCE LINES 5-19



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


    *

      .. image:: /auto_examples/images/sphx_glr_plot_explain_example_001.png
          :alt: ROC curve for class 0, ROC curve for class 1, ROC curve for class 2
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_explain_example_002.png
          :alt: class: 0, class: 1, class: 2
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    Running DummyClassifier()
    accuracy: 0.406 recall_macro: 0.333 precision_macro: 0.135 f1_macro: 0.193
    === new best DummyClassifier() (using recall_macro):
    accuracy: 0.406 recall_macro: 0.333 precision_macro: 0.135 f1_macro: 0.193

    Running GaussianNB()
    accuracy: 0.954 recall_macro: 0.958 precision_macro: 0.956 f1_macro: 0.956
    === new best GaussianNB() (using recall_macro):
    accuracy: 0.954 recall_macro: 0.958 precision_macro: 0.956 f1_macro: 0.956

    Running MultinomialNB()
    accuracy: 0.940 recall_macro: 0.937 precision_macro: 0.954 f1_macro: 0.941
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
    accuracy: 0.579 recall_macro: 0.610 precision_macro: 0.441 f1_macro: 0.487
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
    accuracy: 0.903 recall_macro: 0.903 precision_macro: 0.911 f1_macro: 0.902
    Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
    accuracy: 0.888 recall_macro: 0.889 precision_macro: 0.895 f1_macro: 0.887
    Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985
    === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985

    Running LogisticRegression(class_weight='balanced', max_iter=1000)
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985

    Best model:
    LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
    Best Scores:
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985
                  precision    recall  f1-score   support

               0       0.95      1.00      0.97        18
               1       1.00      0.88      0.94        17
               2       0.91      1.00      0.95        10

        accuracy                           0.96        45
       macro avg       0.95      0.96      0.95        45
    weighted avg       0.96      0.96      0.95        45

    [[18  0  0]
     [ 1 15  1]
     [ 0  0 10]]
    /home/circleci/project/dabl/plot/utils.py:378: UserWarning: FixedFormatter should only be used together with FixedLocator
      ax.set_yticklabels(






|

.. code-block:: default

    from dabl.models import SimpleClassifier
    from dabl.explain import explain
    from sklearn.datasets import load_wine
    from sklearn.model_selection import train_test_split

    wine = load_wine()

    X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target)

    sc = SimpleClassifier()

    sc.fit(X_train, y_train)

    explain(sc, X_test, y_test)


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

   **Total running time of the script:** ( 0 minutes  1.199 seconds)


.. _sphx_glr_download_auto_examples_plot_explain_example.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_explain_example.py <plot_explain_example.py>`



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

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


.. only:: html

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

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