.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_classifier.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_classifier.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_classifier.py:


============================================
Fitting and evaluating an ICR Random Forest
============================================

In this example we fit and evaluate an
:class:`icrlearn.ICRRandomForestClassifier` on the Iris dataset.

.. GENERATED FROM PYTHON SOURCE LINES 11-12

Load the Iris dataset and split it into training and test sets

.. GENERATED FROM PYTHON SOURCE LINES 12-19

.. code-block:: Python

    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    X, y = load_iris(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)








.. GENERATED FROM PYTHON SOURCE LINES 20-21

Train an ICRRandomForestClassifier on the training set

.. GENERATED FROM PYTHON SOURCE LINES 21-25

.. code-block:: Python

    from icrlearn import ICRRandomForestClassifier

    clf = ICRRandomForestClassifier().fit(X_train, y_train)








.. GENERATED FROM PYTHON SOURCE LINES 26-27

Evaluate the classifier on the test set and print some metrics

.. GENERATED FROM PYTHON SOURCE LINES 27-33

.. code-block:: Python

    from sklearn.metrics import classification_report

    y_pred = clf.predict(X_test)
    classification_report = classification_report(y_test, y_pred)
    print(classification_report)





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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        10
               1       0.73      1.00      0.84         8
               2       1.00      0.75      0.86        12

        accuracy                           0.90        30
       macro avg       0.91      0.92      0.90        30
    weighted avg       0.93      0.90      0.90        30





.. GENERATED FROM PYTHON SOURCE LINES 34-35

Plot a confusion matrix of the classifier's predictions

.. GENERATED FROM PYTHON SOURCE LINES 35-44

.. code-block:: Python

    import matplotlib.pyplot as plt
    import seaborn as sns
    from sklearn.metrics import confusion_matrix

    conf_matr = confusion_matrix(y_test, y_pred)
    sns.heatmap(conf_matr, annot=True)
    plt.xlabel("Predicted")
    plt.ylabel("True")
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_classifier_001.png
   :alt: plot classifier
   :srcset: /auto_examples/images/sphx_glr_plot_classifier_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.616 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_classifier.zip <plot_classifier.zip>`


.. only:: html

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

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