This is documentation for an old release of Scikit-learn (version 0.24). Try the latest stable release (version 1.6) or development (unstable) versions.
Note
Click here to download the full example code or to run this example in your browser via Binder
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.
![plot svm nonlinear](../../_images/sphx_glr_plot_svm_nonlinear_001.png)
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(gamma='auto')
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,
linestyles='dashed')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,
edgecolors='k')
plt.xticks(())
plt.yticks(())
plt.axis([-3, 3, -3, 3])
plt.show()
Total running time of the script: ( 0 minutes 4.217 seconds)