title: H2O stacked ensemble with models using different inputs
body: <p>Using <code>h2o flow</code>, is there a way to create a stacked ensemble model based on individual models that may not take the same inputs but predict on the same response labels.</p>
  
  <p>Eg. I am trying to predict for miscoded healthcare claims (ie. charges) and would like to train models for a stacked ensemble of the form:</p>
  
  <pre><code>model1(diagnosis1, diagnosis2, ..., diagnosis5) -&gt; denied or paid (by insurer)
  model2(procedure, procedure_detail1, ..., procedure_detail5) -&gt; denied or paid 
  model3(service_date, insurance_amount, insurer_id) -&gt; (same)
  model4(pat_age, pat_sex, ...) -&gt; (same)
  ...
  </code></pre>
  
  <p>Is there a way to do this in <code>h2o flow</code> (can't tell how to do this with what is presented in the <code>h2o flow</code> gui for stacked ensemble)? Is this even a sensible way to go about this or is it confused in some way (relatively new to machine learning)? Thanks.</p>