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)
# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.lines as mlines import matplotlib.pyplot as plt from sklearn import svm from sklearn.datasets import make_blobs from sklearn.inspection import 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()
Total running time of the script: (0 minutes 0.182 seconds)
Related examples
Plot different SVM classifiers in the iris dataset
Plot different SVM classifiers in the iris dataset
