1、不平衡数据分类问题

对于非平衡级分类超平面，使用不平衡SVC找出最优分类超平面，基本的思想是，我们先找到一个普通的分类超平面，自动进行校正，求出最优的分类超平面

测试代码如下：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
rng = np.random.RandomState(0)
n_samples_1 = 1000
n_samples_2 = 100
X = np.r_[1.5 * rng.randn(n_samples_1, 2),0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
print X
print y

clf = svm.SVC(kernel=‘linear‘, C=1.0)
clf.fit(X, y)
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - clf.intercept_[0] / w[1]
wclf = svm.SVC(kernel=‘linear‘, class_weight={1: 10})
wclf.fit(X, y)

ww = wclf.coef_[0]
wa = -ww[0] / ww[1]
wyy = wa * xx - wclf.intercept_[0] / ww[1]

h0 = plt.plot(xx, yy, ‘k-‘, label=‘no weights‘)
h1 = plt.plot(xx, wyy, ‘k--‘, label=‘with weights‘)
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.legend()

plt.axis(‘tight‘)
plt.show()
运行结果如下：

2、回归问题

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

X = np.sort(5 * np.random.rand(40, 1), axis=0)
y = np.sin(X).ravel()

y[::5] += 3 * (0.5 - np.random.rand(8))

svr_rbf = SVR(kernel=‘rbf‘, C=1e3, gamma=0.1)
svr_lin = SVR(kernel=‘linear‘, C=1e3)
svr_poly = SVR(kernel=‘poly‘, C=1e3, degree=2)
y_rbf = svr_rbf.fit(X, y).predict(X)
y_lin = svr_lin.fit(X, y).predict(X)
y_poly = svr_poly.fit(X, y).predict(X)

lw = 2
plt.scatter(X, y, color=‘darkorange‘, label=‘data‘)
plt.hold(‘on‘)
plt.plot(X, y_rbf, color=‘navy‘, lw=lw, label=‘RBF model‘)
plt.plot(X, y_lin, color=‘c‘, lw=lw, label=‘Linear model‘)
plt.plot(X, y_poly, color=‘cornflowerblue‘, lw=lw, label=‘Polynomial model‘)
plt.xlabel(‘data‘)
plt.ylabel(‘target‘)
plt.title(‘Support Vector Regression‘)
plt.legend()
plt.show()

运行结果如下：

机器学习算法--svm实战