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train a set of sklearn models one each per a random partition of the data (computed distributed). then combine all those models using averaging and evaluate the
by brokensegue 7y ago
train a set of sklearn models one each per a random partition of the data (computed distributed). then combine all those models using averaging and evaluate them all against an even larger dataset. how do you do that in SQL
- truth_seeker 7y agoSharding the table can help scale the problem across many machines and as I mentioned earlier you can use PL/R or PL/Python language extension to lift all sorts of ML functions to SQL functions.
- brokensegue 7y agoI'm unfamiliar with PL/Python. Can you have a Python object be the returned value of a sql query? Because that's a requirement of my example.