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

    Click :ref:`here <sphx_glr_download_auto_examples_pruned_tree.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_pruned_tree.py:


=======================================
Fit a prunable tree on the iris dataset
=======================================

Fit a prunable tree on the iris dataset and compare the results of various pruning strategies.



.. code-block:: python

    print(__doc__)

    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    from pruneabletree import PruneableDecisionTreeClassifier

    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)

    clf_none = PruneableDecisionTreeClassifier(prune=None).fit(X_train, y_train)
    clf_rep = PruneableDecisionTreeClassifier(prune='rep', rep_val_percentage=.1).fit(X_train, y_train)
    clf_ebp = PruneableDecisionTreeClassifier(prune='ebp', ebp_confidence=1e-4).fit(X_train, y_train)

    print("Number of nodes remaining with pruning strategies 'none', 'rep' and 'ebp':")
    print(clf_none.n_actual_nodes)
    print(clf_rep.n_actual_nodes)
    print(clf_ebp.n_actual_nodes)

    print("Real vs predicted classes of the three classifiers:")
    y_none = clf_none.predict(X_test)
    y_rep = clf_rep.predict(X_test)
    y_ebp = clf_ebp.predict(X_test)
    print(y_test)
    print(y_none)
    print(y_rep)
    print(y_ebp)

    print("Accuracy scores of the three classifiers:")
    print(accuracy_score(y_test, y_none))
    print(accuracy_score(y_test, y_rep))
    print(accuracy_score(y_test, y_ebp))

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_pruned_tree.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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