.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/tree/plot_iris_dtc.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_tree_plot_iris_dtc.py: ======================================================================= Plot the decision surface of decision trees trained 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 ` 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. We also show the tree structure of a model built on all of the features. .. GENERATED FROM PYTHON SOURCE LINES 18-19 First load the copy of the Iris dataset shipped with scikit-learn: .. GENERATED FROM PYTHON SOURCE LINES 19-24 .. code-block:: Python from sklearn.datasets import load_iris iris = load_iris() .. GENERATED FROM PYTHON SOURCE LINES 25-26 Display the decision functions of trees trained on all pairs of features. .. GENERATED FROM PYTHON SOURCE LINES 26-77 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_iris from sklearn.inspection import DecisionBoundaryDisplay from sklearn.tree import DecisionTreeClassifier # Parameters n_classes = 3 plot_colors = "ryb" plot_step = 0.02 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 ax = plt.subplot(2, 3, pairidx + 1) plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5) DecisionBoundaryDisplay.from_estimator( clf, X, cmap=plt.cm.RdYlBu, response_method="predict", ax=ax, xlabel=iris.feature_names[pair[0]], 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 decision trees trained on pairs of features") plt.legend(loc="lower right", borderpad=0, handletextpad=0) _ = plt.axis("tight") .. image-sg:: /auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png :alt: Decision surface of decision trees trained on pairs of features :srcset: /auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored /home/runner/work/scikit-learn-pst/scikit-learn-pst/examples/tree/plot_iris_dtc.py:63: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored .. GENERATED FROM PYTHON SOURCE LINES 78-80 Display the structure of a single decision tree trained on all the features together. .. GENERATED FROM PYTHON SOURCE LINES 80-87 .. code-block:: Python from sklearn.tree import plot_tree plt.figure() clf = DecisionTreeClassifier().fit(iris.data, iris.target) plot_tree(clf, filled=True) plt.title("Decision tree trained on all the iris features") plt.show() .. image-sg:: /auto_examples/tree/images/sphx_glr_plot_iris_dtc_002.png :alt: Decision tree trained on all the iris features :srcset: /auto_examples/tree/images/sphx_glr_plot_iris_dtc_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.755 seconds) .. _sphx_glr_download_auto_examples_tree_plot_iris_dtc.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/tree/plot_iris_dtc.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/tree/plot_iris_dtc.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_iris_dtc.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_iris_dtc.py ` .. include:: plot_iris_dtc.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_