title: I get a linear regression using the SVR by python scikit-learn when the data is not linear
body: <pre><code>train.sort_values(by=['mass'], ascending=True, inplace=True)
  x = train['mass']
  y = train['pa']
  
  # 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)
  x_train = x.reshape(x.shape[0], 1)
  x = x_train
  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
  plt.scatter(x, y, c='k', label='data')
  plt.hold('on')
  plt.plot(x, y_rbf, c='g', label='RBF model')
  plt.plot(x, y_lin, c='r', label='Linear model')
  plt.plot(x, y_poly, c='b', label='Polynomial model')
  plt.xlabel('data')
  plt.ylabel('target')
  plt.title('Support Vector Regression')
  plt.legend()
  plt.show()
  </code></pre>
  
  <p>The code is copied from <a href="http://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html</a>. 
  And what I change is only the dataset. I do not know what is the matter.</p>