I have a magic regression aggregator that works like this: 1) Take a dataset and split into training and test 2) Using the training set: run a bunch of differ

I have a magic regression aggregator that works like this: 1) Take a dataset and split into training and test 2) Using the training set: run a bunch of different regressors (for a training-training subset) and get predictions (for the remaining test-training subset) 3) Run a higher-level regression against test-training subset predictions. I use either plain linear regression (so my meta-regressor is a linear combination of the regressors) or K-nearest neighbors (so the best regressor for each region of feature space is chosen). 4) If there are hyperparameters, optimize against the test set (not the test-training subset). It's not available as an API. I'm available for consulting though.