title: Multinomial Logistic Regression in spark ml vs mllib
body: <p>Spark Version 2.0.0  has the stated goal to bring feature parity between the <code>ml</code> and the now-deprecated <code>mllib</code> packages.</p>
  
  <p>Presently however the <code>ml</code> package provides ElasticNet support but only with binary regression.  To obtain multinomial apparently we have to accept using the deprecated mllib?  </p>
  
  <p>The downsides of using mllib: </p>
  
  <ul>
  <li>It is deprecated. So we will have the "why are you using the old stuff" questions to field</li>
  <li>They do not use the <code>ml</code> workflow so it does not integrate cleanly</li>
  <li>For the above reasons we would eventually have to do a rewrite.</li>
  </ul>
  
  <p>Is there an approach available to achieving the one-vs-all multinomial with the <code>ml</code> package?   </p>