title: Issue with Scikit Learn Package for SVR Regression
body: <p>I am trying to fit a SVM regression model using Scikit Learn Package but it is not working like I am expecting. </p>
  
  <p>Could you please help me to find the error? The code that I  would like to use is:</p>
  
  <pre><code>from sklearn.svm import SVR
  import numpy as np
  
  
  X = []
  x = np.arange(0, 20)
  y = [3, 4, 8, 4, 6, 9, 8, 12, 15, 26, 35, 40, 45, 54, 49, 59, 60, 62, 63, 68]
  X.append(x)
  
  clf = SVR(verbose=1)
  clf.fit(np.transpose(X), y)
  
  print("Expecting Result:")
  print(y)
  print("Predicted Result:")
  print(clf.predict(np.transpose(X)))
  </code></pre>
  
  <p>The Output that I have is:</p>
  
  <pre><code>[LibSVM]*
  optimization finished, #iter = 10
  obj = -421.488272, rho = -30.500000
  nSV = 20, nBSV = 20
  Expecting Result:
  [3, 4, 8, 4, 6, 9, 8, 12, 15, 26, 35, 40, 45, 54, 49, 59, 60, 62, 63, 68]
  Predicted Result:
  [ 29.1136814   28.74580196  28.72748632  28.72736291  28.7273628
    28.7273628   28.72736302  28.72760984  28.76424112  29.5         31.5
    32.23575888  32.27239016  32.27263698  32.2726372   32.2726372
    32.27263709  32.27251368  32.25419804  31.8863186 ]
  </code></pre>
  
  <p>We can see that the predicted results are very far from the training data. 
  How can I improve the fitting?</p>
  
  <p>Thanks</p>
  
  <p>David</p>