.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/exercises/plot_iris_exercise.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_exercises_plot_iris_exercise.py: ================================ SVM Exercise ================================ A tutorial exercise for using different SVM kernels. This exercise is used in the :ref:`using_kernels_tut` part of the :ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`. .. GENERATED FROM PYTHON SOURCE LINES 12-76 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_001.png :alt: linear :srcset: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_002.png :alt: rbf :srcset: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_003.png :alt: poly :srcset: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_003.png :class: sphx-glr-multi-img .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, svm iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 0, :2] y = y[y != 0] n_sample = len(X) np.random.seed(0) order = np.random.permutation(n_sample) X = X[order] y = y[order].astype(float) X_train = X[: int(0.9 * n_sample)] y_train = y[: int(0.9 * n_sample)] X_test = X[int(0.9 * n_sample) :] y_test = y[int(0.9 * n_sample) :] # fit the model for kernel in ("linear", "rbf", "poly"): clf = svm.SVC(kernel=kernel, gamma=10) clf.fit(X_train, y_train) plt.figure() plt.clf() plt.scatter( X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired, edgecolor="k", s=20 ) # Circle out the test data plt.scatter( X_test[:, 0], X_test[:, 1], s=80, facecolors="none", zorder=10, edgecolor="k" ) plt.axis("tight") x_min = X[:, 0].min() x_max = X[:, 0].max() y_min = X[:, 1].min() y_max = X[:, 1].max() XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour( XX, YY, Z, colors=["k", "k", "k"], linestyles=["--", "-", "--"], levels=[-0.5, 0, 0.5], ) plt.title(kernel) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 5.237 seconds) .. _sphx_glr_download_auto_examples_exercises_plot_iris_exercise.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/exercises/plot_iris_exercise.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/exercises/plot_iris_exercise.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_iris_exercise.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_iris_exercise.py ` .. include:: plot_iris_exercise.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_