text: Title: ensembling methodologies to try
  Body: The infrastructure for ensembling is in place. The performance of an ensemble is only matching the performance of a well-trained single estimator, or lagging slightly behind it at times. 
  
  Here are some ideas to try to improve that performance:
  
  - [ ] find the best way of ensembling predictions together (median, mean, max, min)
  - [ ] try different algorithms to ensemble together the final predictions using ml
  - [ ] try different approaches of limiting overfitting for that final algorithm
  - [ ] try ensembles of algorithms that are more different from each other
  - [ ] try subsetting the data more for each sub-estimator (random selection within that subset
  - [ ] train a model just on the difficult cases
  - [ ] try allocating different amounts of data for ensembling vs. training sub-estimators
  - [ ] try splitting ensemble data vs. training data by date, randomly, or in the order we were given the data in
  - [ ] highly unlikely to be useful, but since we're trying a ktichen sink approach: try ensembling on the same data we trained the sub-estimators on. again, just to be clear, this is intentionally introducing a ton of overfitting and i expect it to crash and burn horribly. but let's verify that expectation, and maybe we'll observe some interesting behavior along the way. 
  - [ ] only keep estimators that are within X% (user-configurable) accuracy as our best estimator
  