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I know a machine learning engineer from a certain massive lending company. I asked him “How do you decide which strategy to use and which algorithms to pay atte
by shawn 8y ago
I know a machine learning engineer from a certain massive lending company. I asked him “How do you decide which strategy to use and which algorithms to pay attention to? There are so many, and it’s hard to figure out which ones are production grade.”
He said gradient boosting. I pointed out that that implies there would be some period of time where the algorithm makes bad decisions. He said yup, that’s why it’s hard to start a lending company. Gotta find the data on the bad performers.
Apparently their original decision model was just a hand crafted set of if-statements.
I suspect most ML endeavors that seem impressive have origin stories like that, but it’s only anecdata.
- kgwgk 8y ago> He said gradient boosting. I pointed out that that implies there would be some period of time where the algorithm makes bad decisions. How are the first and second sentences related? Would other algorithms make better decisions?
- shawn 8y agoSure, self-play. Generate the data and search through the problem space for optimal decisions before they come up.
- bitxbit 8y agoAt the end of the day it’s a complex form of heuristic.
- AstralStorm 8y agoEven if it is an actual algorithm that is provably optimal (in some sense), it is probably slave to accurate and complete inputs...