.. 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

        :ref:`Go to the end <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: 0.376 recall_macro: 0.333 precision_macro: 0.125 f1_macro: 0.182
    === new best DummyClassifier() (using recall_macro):
    accuracy: 0.376 recall_macro: 0.333 precision_macro: 0.125 f1_macro: 0.182

    Running GaussianNB()
    accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.973 f1_macro: 0.971
    === new best GaussianNB() (using recall_macro):
    accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.973 f1_macro: 0.971

    Running MultinomialNB()
    accuracy: 0.963 recall_macro: 0.964 precision_macro: 0.967 f1_macro: 0.965
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
    accuracy: 0.571 recall_macro: 0.600 precision_macro: 0.417 f1_macro: 0.477
    Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
    accuracy: 0.925 recall_macro: 0.923 precision_macro: 0.940 f1_macro: 0.926
    Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
    accuracy: 0.902 recall_macro: 0.902 precision_macro: 0.917 f1_macro: 0.905
    Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.987 f1_macro: 0.986
    === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
    accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.987 f1_macro: 0.986

    Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
    accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000
    === new best LogisticRegression(C=1, class_weight='balanced', max_iter=1000) (using recall_macro):
    accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000


    Best model:
    LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
    Best Scores:
    accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000
                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        14
               1       1.00      0.95      0.98        21
               2       0.91      1.00      0.95        10

        accuracy                           0.98        45
       macro avg       0.97      0.98      0.98        45
    weighted avg       0.98      0.98      0.98        45

    [[14  0  0]
     [ 0 20  1]
     [ 0  0 10]]
    /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:: Python

    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 0.698 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-jupyter

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

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

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


.. only:: html

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

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