title: SVC with class_weight in scikit-learn
body: <p>I would like to use <code>class_weight</code> to create a weighted SVC classifier in sikit-learn. Nevertheless, I'm not sure if I'm configuring correctly my model. Please consider the example below:</p>
  
  <pre><code>x = np.array([[0,0,1],[0,1,1],[1,0,0]])
  y = np.array([1,1,0])
  
  cw = {}
  for l in set(y):
      cw[l] = np.sum(y == l)
  print(cw)
  
  m = SVC(probability = True, max_iter = 1000, class_weight = cw)
  m = m.fit(x,y)
  </code></pre>
  
  <p>I obtained the model:</p>
  
  <pre><code>SVC(C=1.0, cache_size=200, class_weight={0: 1, 1: 2}, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
    max_iter=1000, probability=True, random_state=None, shrinking=True,
    tol=0.001, verbose=False)
  </code></pre>
  
  <p>With <code>class_weight={0: 1, 1: 2}</code> corresponding to the number of data points in each class.</p>
  
  <p><strong>QUESTION:</strong> Is it correct to proceed in this way?</p>