Plot different SVM classifiers in the iris datasetΒΆ
Comparison of different linear SVM classifiers on the iris dataset. It will plot the decision surface four different SVM classifiers.
![../../_images/plot_iris.png](../../_images/plot_iris.png)
Python source code: plot_iris.py
import numpy as np
import pylab as pl
from scikits.learn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
Y = iris.target
h=.02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
svc = svm.SVC(kernel='linear').fit(X, Y)
rbf_svc = svm.SVC(kernel='poly').fit(X, Y)
nu_svc = svm.NuSVC(kernel='linear').fit(X,Y)
lin_svc = svm.LinearSVC().fit(X, Y)
# create a mesh to plot in
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'SVC with polynomial (degree 3) kernel',
'NuSVC with linear kernel',
'LinearSVC (linear kernel)']
pl.set_cmap(pl.cm.Paired)
for i, clf in enumerate((svc, rbf_svc, nu_svc, lin_svc)):
# Plot the decision boundary. For that, we will asign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
pl.subplot(2, 2, i+1)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.set_cmap(pl.cm.Paired)
pl.contourf(xx, yy, Z)
pl.axis('tight')
# Plot also the training points
pl.scatter(X[:,0], X[:,1], c=Y)
pl.title(titles[i])
pl.axis('tight')
pl.show()