.. _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`. .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_000.png :scale: 47 * .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_001.png :scale: 47 * .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_002.png :scale: 47 .. code-block:: python print(__doc__) import numpy as np import matplotlib.pyplot as plt 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(np.float) X_train = X[:int(.9 * n_sample)] y_train = y[:int(.9 * n_sample)] X_test = X[int(.9 * n_sample):] y_test = y[int(.9 * n_sample):] # fit the model for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')): clf = svm.SVC(kernel=kernel, gamma=10) clf.fit(X_train, y_train) plt.figure(fig_num) plt.clf() plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired) # Circle out the test data plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10) 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=[-.5, 0, .5]) plt.title(kernel) plt.show() **Total running time of the script:** (0 minutes 6.988 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_iris_exercise.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_iris_exercise.ipynb `