title: Sklearn - svm weighted features
body: <p>Hi I am quite new in machine learning with scikit-learn but I have a question.</p>
  
  <p>Is it possible to make svm recognize some features of my vector data with higher importance or weight. I do not know the correct therm :/</p>
  
  <p>Here is example of what I want:</p>
  
  <p>Let's say we have bunch of vecotrs in 2d array:</p>
  
  <pre><code>dt = [
  [x1, y1, z1, q1],
  [x2, y2, z2, q2],
  [x3, y3, z3, q3],
  [x4, y4, z4, q4]]
  </code></pre>
  
  <p>And target data vector:</p>
  
  <pre><code>target = [1,0,0,1]
  </code></pre>
  
  <p>What I want to know how can I make, for example, all q features for each of vectors in dt more important than rest of the features.</p>
  
  <p>I know how to balance data by sets or to set sample_weight but neither can help me with this problem.</p>
  
  <p>Cheers</p>