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# SVM Margins Example¶

The plots below illustrate the effect the parameter C has on the separation line. A large value of C basically tells our model that we do not have that much faith in our data’s distribution, and will only consider points close to line of separation.

A small value of C includes more/all the observations, allowing the margins to be calculated using all the data in the area.

Python source code: plot_svm_margin.py

```print(__doc__)

# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

# we create 40 separable points
np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y =  * 20 +  * 20

# figure number
fignum = 1

# fit the model
for name, penalty in (('unreg', 1), ('reg', 0.05)):

clf = svm.SVC(kernel='linear', C=penalty)
clf.fit(X, Y)

# get the separating hyperplane
w = clf.coef_
a = -w / w
xx = np.linspace(-5, 5)
yy = a * xx - (clf.intercept_) / w

# plot the parallels to the separating hyperplane that pass through the
# support vectors
margin = 1 / np.sqrt(np.sum(clf.coef_ ** 2))
yy_down = yy + a * margin
yy_up = yy - a * margin

# plot the line, the points, and the nearest vectors to the plane
plt.figure(fignum, figsize=(4, 3))
plt.clf()
plt.plot(xx, yy, 'k-')
plt.plot(xx, yy_down, 'k--')
plt.plot(xx, yy_up, 'k--')

plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
facecolors='none', zorder=10)
plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired)

plt.axis('tight')
x_min = -4.8
x_max = 4.2
y_min = -6
y_max = 6

XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.predict(np.c_[XX.ravel(), YY.ravel()])

# Put the result into a color plot
Z = Z.reshape(XX.shape)
plt.figure(fignum, figsize=(4, 3))
plt.pcolormesh(XX, YY, Z, cmap=plt.cm.Paired)

plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)

plt.xticks(())
plt.yticks(())
fignum = fignum + 1

plt.show()
```

Total running time of the example: 0.17 seconds ( 0 minutes 0.17 seconds)