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

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

.. _sphx_glr_auto_examples_tree_plot_iris.py:


================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================

Plot the decision surface of a decision tree trained on pairs
of features of the iris dataset.

See :ref:`decision tree <tree>` for more information on the estimator.

For each pair of iris features, the decision tree learns decision
boundaries made of combinations of simple thresholding rules inferred from
the training samples.



.. image:: /auto_examples/tree/images/sphx_glr_plot_iris_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none







|


.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.datasets import load_iris
    from sklearn.tree import DecisionTreeClassifier

    # Parameters
    n_classes = 3
    plot_colors = "ryb"
    plot_step = 0.02

    # Load data
    iris = load_iris()

    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
                                    [1, 2], [1, 3], [2, 3]]):
        # We only take the two corresponding features
        X = iris.data[:, pair]
        y = iris.target

        # Train
        clf = DecisionTreeClassifier().fit(X, y)

        # Plot the decision boundary
        plt.subplot(2, 3, pairidx + 1)

        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                             np.arange(y_min, y_max, plot_step))
        plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)

        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)

        plt.xlabel(iris.feature_names[pair[0]])
        plt.ylabel(iris.feature_names[pair[1]])

        # Plot the training points
        for i, color in zip(range(n_classes), plot_colors):
            idx = np.where(y == i)
            plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                        cmap=plt.cm.RdYlBu, edgecolor='black', s=15)

    plt.suptitle("Decision surface of a decision tree using paired features")
    plt.legend(loc='lower right', borderpad=0, handletextpad=0)
    plt.axis("tight")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.991 seconds)


.. _sphx_glr_download_auto_examples_tree_plot_iris.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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