Note

Go to the end to download the full example code or to run this example in your browser via JupyterLite or Binder.

SVM: Separating hyperplane for unbalanced classes#

Find the optimal separating hyperplane using an SVC for classes that are unbalanced.

We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automatically correction for unbalanced classes.

Note

This example will also work by replacing SVC(kernel="linear") with SGDClassifier(loss="hinge"). Setting the loss parameter of the SGDClassifier equal to hinge will yield behaviour such as that of an SVC with a linear kernel.

For example try instead of the SVC:

clf = SGDClassifier(n_iter=100, alpha=0.01)

plot separating hyperplane unbalanced

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.linesasmlines
importmatplotlib.pyplotasplt
fromsklearnimport svm
fromsklearn.datasetsimport make_blobs
fromsklearn.inspectionimport DecisionBoundaryDisplay
# we create two clusters of random points
n_samples_1 = 1000
n_samples_2 = 100
centers = [[0.0, 0.0], [2.0, 2.0]]
clusters_std = [1.5, 0.5]
X, y = make_blobs (
 n_samples=[n_samples_1, n_samples_2],
 centers=centers,
 cluster_std=clusters_std,
 random_state=0,
 shuffle=False,
)
# fit the model and get the separating hyperplane
clf = svm.SVC (kernel="linear", C=1.0)
clf.fit(X, y)
# fit the model and get the separating hyperplane using weighted classes
wclf = svm.SVC (kernel="linear", class_weight={1: 10})
wclf.fit(X, y)
# plot the samples
plt.scatter (X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors="k")
# plot the decision functions for both classifiers
ax = plt.gca ()
disp = DecisionBoundaryDisplay.from_estimator (
 clf,
 X,
 plot_method="contour",
 colors="k",
 levels=[0],
 alpha=0.5,
 linestyles=["-"],
 ax=ax,
)
# plot decision boundary and margins for weighted classes
wdisp = DecisionBoundaryDisplay.from_estimator (
 wclf,
 X,
 plot_method="contour",
 colors="r",
 levels=[0],
 alpha=0.5,
 linestyles=["-"],
 ax=ax,
)
plt.legend (
 [
 mlines.Line2D ([], [], color="k", label="non weighted"),
 mlines.Line2D ([], [], color="r", label="weighted"),
 ],
 ["non weighted", "weighted"],
 loc="upper right",
)
plt.show ()