Nico.cast🎩
@n
Working on a ML model which will output an 'address score' similar to how credit scores work (~low is 300-500, mid 500-650, high 650+) some interesting data: me: 529.52 @alexpaden: 567.79 @cassie: 685.06 @jc: 698.19 @dwr.eth: 663.29 @dcposch.eth: 752.81
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Nico.cast🎩
@n
Just if it's not clear: this pre-trained model will be open-sourced. I am training with FC addresses to start, and trying to get an idea on the scoring scale - are these too low? too high?
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tldr (tim reilly)
@tldr
To answer, could you give a sense of what kind of thing the score is supposed to indicate? In credit scores, the purpose is to indicate something like future likelihood of promised payment. What is the analogous “thing measured” here?
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