.. _example_svm_plot_iris.py: ================================================== Plot different SVM classifiers in the iris dataset ================================================== Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset: - Sepal length - Sepal width This example shows how to plot the decision surface for four SVM classifiers with different kernels. The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly different decision boundaries. This can be a consequence of the following differences: - ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the regular hinge loss. - ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass reduction while ``SVC`` uses the One-vs-One multiclass reduction. Both linear models have linear decision boundaries (intersecting hyperplanes) while the non-linear kernel models (polynomial or Gaussian RBF) have more flexible non-linear decision boundaries with shapes that depend on the kind of kernel and its parameters. .. NOTE:: while plotting the decision function of classifiers for toy 2D datasets can help get an intuitive understanding of their respective expressive power, be aware that those intuitions don't always generalize to more realistic high-dimensional problem. .. image:: images/plot_iris_001.png :align: center **Python source code:** :download:`plot_iris.py ` .. literalinclude:: plot_iris.py :lines: 36- **Total running time of the example:** 0.65 seconds ( 0 minutes 0.65 seconds)