text: Title: MostInformativeFeatures visualizer
  Body: **Is your feature request related to a problem? Please describe.**
  This feature is motivated by the discussion in #510. The problem is how to visualize feature importance for multiple classes and/or instances. This requires solving two problems: 1. selecting the "most informative features" and 2. producing an appropriate visualization.
  
  **Describe the solution you'd like**
  MIFV = MostInformativeFeatures
  MIFV().predict(X) would give a viz for the features most responsible for the prediction
  MIFV().poof()/fit(X) would show a viz for the features 
  
  For both cases, the proposed visual is a heat map where one axis is class labels or data and the other axis represents features. 
  
  Questions/Issues:
  * The estimators would have to (mathematically) support such a notion of feature importance.
  * Top 10 features doesn't make sense if all the features are similar in strength.
  * Does it make sense to numerically compare feature strengths over multiple classes?
  