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What about data? Does using AutoML increase the amount of data needed, due to needing more complex cross-validation setups, or can you get away with roughly the
by radarsat1 4y ago
What about data? Does using AutoML increase the amount of data needed, due to needing more complex cross-validation setups, or can you get away with roughly the same split?
I ask because I find that where the most domain knowledge is needed is when less labeled data is available, and that's also where I would assume AutoML doesn't perform as well.
- teruakohatu 4y agoSome, if not all, AutoML solutions will require a minimum amount of data to try certain methods. But you are entirely correct. The last commercial use of AutoML I saw demoed had very limited data and while the metrics were ok, I could have made just as good predictions using linear regression in Excel or even just a calculator and some basic heuristics. That's not to say AutoML is bad, I have used H2O with success, it only replaces a small part of the Data Science pipeline.