.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py: ========================================================= SVM Tie Breaking Example ========================================================= Tie breaking is costly if ``decision_function_shape='ovr'``, and therefore it is not enabled by default. This example illustrates the effect of the ``break_ties`` parameter for a multiclass classification problem and ``decision_function_shape='ovr'``. The two plots differ only in the area in the middle where the classes are tied. If ``break_ties=False``, all input in that area would be classified as one class, whereas if ``break_ties=True``, the tie-breaking mechanism will create a non-convex decision boundary in that area. .. image:: /auto_examples/svm/images/sphx_glr_plot_svm_tie_breaking_001.png :alt: break_ties = False, break_ties = True :class: sphx-glr-single-img .. code-block:: default print(__doc__) # Code source: Andreas Mueller, Adrin Jalali # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.datasets import make_blobs X, y = make_blobs(random_state=27) fig, sub = plt.subplots(2, 1, figsize=(5, 8)) titles = ("break_ties = False", "break_ties = True") for break_ties, title, ax in zip((False, True), titles, sub.flatten()): svm = SVC(kernel="linear", C=1, break_ties=break_ties, decision_function_shape='ovr').fit(X, y) xlim = [X[:, 0].min(), X[:, 0].max()] ylim = [X[:, 1].min(), X[:, 1].max()] xs = np.linspace(xlim[0], xlim[1], 1000) ys = np.linspace(ylim[0], ylim[1], 1000) xx, yy = np.meshgrid(xs, ys) pred = svm.predict(np.c_[xx.ravel(), yy.ravel()]) colors = [plt.cm.Accent(i) for i in [0, 4, 7]] points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap="Accent") classes = [(0, 1), (0, 2), (1, 2)] line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5) ax.imshow(-pred.reshape(xx.shape), cmap="Accent", alpha=.2, extent=(xlim[0], xlim[1], ylim[1], ylim[0])) for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes): line2 = -(line * coef[1] + intercept) / coef[0] ax.plot(line2, line, "-", c=colors[col[0]]) ax.plot(line2, line, "--", c=colors[col[1]]) ax.set_xlim(xlim) ax.set_ylim(ylim) ax.set_title(title) ax.set_aspect("equal") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.930 seconds) .. _sphx_glr_download_auto_examples_svm_plot_svm_tie_breaking.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_svm_tie_breaking.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_svm_tie_breaking.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_svm_tie_breaking.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_