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SVM: Maximum margin separating hyperplane#

Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel.

plot separating hyperplane

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
fromsklearnimport svm
fromsklearn.datasetsimport make_blobs
fromsklearn.inspectionimport DecisionBoundaryDisplay
# we create 40 separable points
X, y = make_blobs (n_samples=40, centers=2, random_state=6)
# fit the model, don't regularize for illustration purposes
clf = svm.SVC (kernel="linear", C=1000)
clf.fit(X, y)
plt.scatter (X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
# plot the decision function
ax = plt.gca ()
DecisionBoundaryDisplay.from_estimator (
 clf,
 X,
 plot_method="contour",
 colors="k",
 levels=[-1, 0, 1],
 alpha=0.5,
 linestyles=["--", "-", "--"],
 ax=ax,
)
# plot support vectors
ax.scatter(
 clf.support_vectors_[:, 0],
 clf.support_vectors_[:, 1],
 s=100,
 linewidth=1,
 facecolors="none",
 edgecolors="k",
)
plt.show ()