.. _sphx_glr_auto_examples_svm_plot_svm_nonlinear.py: ============== Non-linear SVM ============== Perform binary classification using non-linear SVC with RBF kernel. The target to predict is a XOR of the inputs. The color map illustrates the decision function learned by the SVC. .. image:: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png :align: center .. code-block:: python print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) np.random.seed(0) X = np.random.randn(300, 2) Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) # fit the model clf = svm.NuSVC() clf.fit(X, Y) # plot the decision function for each datapoint on the grid Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.imshow(Z, interpolation='nearest', extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto', origin='lower', cmap=plt.cm.PuOr_r) contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linetypes='--') plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired) plt.xticks(()) plt.yticks(()) plt.axis([-3, 3, -3, 3]) plt.show() **Total running time of the script:** (0 minutes 1.233 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_svm_nonlinear.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_svm_nonlinear.ipynb `