title: spark ml : how to find feature importance
body: <p>I am new to ML and I am building a prediction system using Spark ml. I read that a major part of feature engineering is to find the importance of each feature in doing the required prediction. In my problem, I have three categorical features and two string features. I use the <code>OneHotEncoding</code> technique for transforming the categorical features and simple <code>HashingTF</code> mechanism to transform the string features. And, then these are input as various stages of the Pipeline, including ml <code>NaiveBayes</code> and a <code>VectorAssembler</code> (to assemble all the features into a single column), fit and transformed using the training and test data sets respectively. </p>
  
  <p>Everything is good, except, how do I decide the importance of each feature? I know I have only a handful of features now, but I will be adding more soon. The closest thing I came across was the <code>ChiSqSelector</code> available with spark ml module, but it seems to only work for categorical features.  </p>
  
  <p>Thanks, any leads appreciated!</p>