.. 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



.. image-sg:: /auto_examples/images/sphx_glr_plot_explain_example_001.png
   :alt: class: 0, class: 1, class: 2
   :srcset: /auto_examples/images/sphx_glr_plot_explain_example_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Running DummyClassifier()
    accuracy: 0.383 recall_macro: 0.333 precision_macro: 0.128 f1_macro: 0.185
    === new best DummyClassifier() (using recall_macro):
    accuracy: 0.383 recall_macro: 0.333 precision_macro: 0.128 f1_macro: 0.185

    Running GaussianNB()
    accuracy: 0.970 recall_macro: 0.973 precision_macro: 0.973 f1_macro: 0.971
    === new best GaussianNB() (using recall_macro):
    accuracy: 0.970 recall_macro: 0.973 precision_macro: 0.973 f1_macro: 0.971

    Running MultinomialNB()
    accuracy: 0.932 recall_macro: 0.936 precision_macro: 0.943 f1_macro: 0.936
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
    accuracy: 0.571 recall_macro: 0.609 precision_macro: 0.439 f1_macro: 0.486
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
    accuracy: 0.932 recall_macro: 0.935 precision_macro: 0.936 f1_macro: 0.933
    Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
    accuracy: 0.925 recall_macro: 0.931 precision_macro: 0.928 f1_macro: 0.926
    Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
    accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993
    === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
    accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993

    Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
    accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993

    Best model:
    LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
    Best Scores:
    accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993
                  precision    recall  f1-score   support

               0       0.92      1.00      0.96        12
               1       0.95      0.90      0.92        20
               2       0.92      0.92      0.92        13

        accuracy                           0.93        45
       macro avg       0.93      0.94      0.94        45
    weighted avg       0.93      0.93      0.93        45

    [[12  0  0]
     [ 1 18  1]
     [ 0  1 12]]
    /home/circleci/project/dabl/explain.py:45: UserWarning: Can't plot roc curve, install sklearn 0.22-dev
      warn("Can't plot roc curve, install sklearn 0.22-dev")






|

.. 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  0.623 seconds)


.. _sphx_glr_download_auto_examples_plot_explain_example.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_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>`_