title: Python scikit learn n_jobs
body: <p>This is not a real issue, but I'd like to understand:</p>
  
  <ul>
  <li>running sklearn from Anaconda distrib on a Win7 4 cores 8 GB system</li>
  <li>fitting a KMeans model on a 200.000 samples*200 values table.</li>
  <li>running with n-jobs = -1: (after adding the <code>if __name__ == '__main__':</code> line to my script) I see the script starting 4 processes with
  10 threads each. Each process uses about 25% of the CPU (total:
  100%). Seems to work as expected</li>
  <li>running with n-jobs = 1: stays on a single process (not a surprise), with 20 threads, and also uses 100% of the CPU.</li>
  </ul>
  
  <p>My question: what is the point of using n-jobs (and joblib) if the the library uses all cores anyway? Am I missing something? Is it a Windows-specific behaviour? </p>