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When applied to the models themselves it's not a particularly helpful analogy. Machine learning models are largely curve fitting in high dimensional spaces. Ove
by madhadron 7y ago
When applied to the models themselves it's not a particularly helpful analogy. Machine learning models are largely curve fitting in high dimensional spaces. Overfitting, overspecialization, and the like are problems, and you could relate them to ecological notions and selection, but it's not terribly helpful in practice.
Where it does apply is to the cascade of dependencies among data sets, code that generates them, and sources of signal that you see in large data platforms.
- Ecalpal 7y agoThank you for articulating my point better than I could myself about the 'cascade of dependencies'. It seems to me that these platforms/packages are essentially trying to build something almost-as-complex as a genome from scratch, and that trying to avoid those cascades is paramount. However, is this possible in practise, and if so, what would help streamline the process? Would a generalisable package solution help with this, and what's limiting development of such a solution? Is it because we don't know what such a solution should/ought to look like?
- madhadron 7y ago> Would a generalisable package solution help with this Not really. It's more what happens when hundreds or thousands of people try to get lots of different things done using shared resources over a period of years.