Example: Support Vector Regression using linear and non-linear kernels
Support Vector Regression (SVR) using linear and non-linear kernels
Toy example of 1D regression using linear, polynomial and RBF kernels.
print(__doc__) import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt
Generate sample data
X = np.sort(5 * np.random.rand(40, 1), axis=0) y = np.sin(X).ravel()
Add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))
Fit regression model
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)
look at the results
登录查看完整内容