.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py: ================================================= 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. .. currentmodule:: sklearn.linear_model .. note:: This example will also work by replacing ``SVC(kernel="linear")`` with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour such as that of a SVC with a linear kernel. For example try instead of the ``SVC``:: clf = SGDClassifier(n_iter=100, alpha=0.01) .. image:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none | .. code-block:: default print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm from sklearn.datasets import make_blobs # 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() xlim = ax.get_xlim() ylim = ax.get_ylim() # create grid to evaluate model xx = np.linspace(xlim[0], xlim[1], 30) yy = np.linspace(ylim[0], ylim[1], 30) YY, XX = np.meshgrid(yy, xx) xy = np.vstack([XX.ravel(), YY.ravel()]).T # get the separating hyperplane Z = clf.decision_function(xy).reshape(XX.shape) # plot decision boundary and margins a = ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles=['-']) # get the separating hyperplane for weighted classes Z = wclf.decision_function(xy).reshape(XX.shape) # plot decision boundary and margins for weighted classes b = ax.contour(XX, YY, Z, colors='r', levels=[0], alpha=0.5, linestyles=['-']) plt.legend([a.collections[0], b.collections[0]], ["non weighted", "weighted"], loc="upper right") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.171 seconds) .. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane_unbalanced.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_separating_hyperplane_unbalanced.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_separating_hyperplane_unbalanced.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_