title: Is there a python equivalent for R's h2o.stack?
body: <p>I am working with stacked learners. According to the docs for <a href="http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html" rel="nofollow noreferrer">H2OStackedEnsembleEstimator</a> h2o's python implementation allows you to easily build ensemble models. However this is limited to building base classifiers with the same underlying training data. I have time based features whose minimum date varies depending on the data source. Each sample of data is a point in time. To take advantage of as much data as I can, I split the features up until two groups (depending on relevance and minimum date) and train two separate models. I would like to combine these models, but H2OStackedEnsembleEstimator requires the features to be the same. </p>
  
  <p>According to <a href="https://stackoverflow.com/questions/42408295/what-is-the-different-between-h2o-ensemble-and-h2o-stack-in-package-h2oensemble">this post</a> about R's stacked ensemble implementation there is an option to only perform the metalearning step which should require only the k-fold cross-validation predicitons for each base model and the true target value. </p>
  
  <p>In case it crosses anyone's mind...for my particular problem, I realize I am going to run into an issue with the metalearning step with this mismatch in minimum date, and I have ideas to circumvent this. </p>