.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/svm/plot_linearsvc_support_vectors.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py: ===================================== Plot the support vectors in LinearSVC ===================================== Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vectors. This example demonstrates how to obtain the support vectors in LinearSVC. .. GENERATED FROM PYTHON SOURCE LINES 11-59 .. image-sg:: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png :alt: C=1, C=100 :srcset: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png :class: sphx-glr-single-img .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_blobs from sklearn.inspection import DecisionBoundaryDisplay from sklearn.svm import LinearSVC X, y = make_blobs(n_samples=40, centers=2, random_state=0) plt.figure(figsize=(10, 5)) for i, C in enumerate([1, 100]): # "hinge" is the standard SVM loss clf = LinearSVC(C=C, loss="hinge", random_state=42, dual="auto").fit(X, y) # obtain the support vectors through the decision function decision_function = clf.decision_function(X) # we can also calculate the decision function manually # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0] # The support vectors are the samples that lie within the margin # boundaries, whose size is conventionally constrained to 1 support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0] support_vectors = X[support_vector_indices] plt.subplot(1, 2, i + 1) plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired) ax = plt.gca() DecisionBoundaryDisplay.from_estimator( clf, X, ax=ax, grid_resolution=50, plot_method="contour", colors="k", levels=[-1, 0, 1], alpha=0.5, linestyles=["--", "-", "--"], ) plt.scatter( support_vectors[:, 0], support_vectors[:, 1], s=100, linewidth=1, facecolors="none", edgecolors="k", ) plt.title("C=" + str(C)) plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.208 seconds) .. _sphx_glr_download_auto_examples_svm_plot_linearsvc_support_vectors.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/svm/plot_linearsvc_support_vectors.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_linearsvc_support_vectors.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_linearsvc_support_vectors.py ` .. include:: plot_linearsvc_support_vectors.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_