.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/svm/plot_svm_nonlinear.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_svm_nonlinear.py: ============== Non-linear SVM ============== Perform binary classification using non-linear SVC with RBF kernel. The target to predict is a XOR of the inputs. The color map illustrates the decision function learned by the SVC. .. GENERATED FROM PYTHON SOURCE LINES 13-46 .. image-sg:: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png :alt: plot svm nonlinear :srcset: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png :class: sphx-glr-single-img .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn import svm xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) np.random.seed(0) X = np.random.randn(300, 2) Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) # fit the model clf = svm.NuSVC(gamma="auto") clf.fit(X, Y) # plot the decision function for each datapoint on the grid Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.imshow( Z, interpolation="nearest", extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect="auto", origin="lower", cmap=plt.cm.PuOr_r, ) contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed") plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors="k") plt.xticks(()) plt.yticks(()) plt.axis([-3, 3, -3, 3]) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.484 seconds) .. _sphx_glr_download_auto_examples_svm_plot_svm_nonlinear.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_svm_nonlinear.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/svm/plot_svm_nonlinear.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_svm_nonlinear.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_svm_nonlinear.py ` .. include:: plot_svm_nonlinear.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_