This is documentation for an old release of Scikit-learn (version 1.4). Try the latest stable release (version 1.6) or development (unstable) versions.

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.

import matplotlib.pyplot as plt
import numpy as np

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()