Nico.vibe
@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.vibe
@n
I am planning to open-source the model. My training data and scoring system depend on a dozens of factor, notably num_transactions_score, transaction_frequency, total_value Goal: decentralized consumer reports anyone?
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vincent
@pixel
Would be interested in the algo, how do you arrive at those numbers?
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Nico.vibe
@n
I will open-source the model once it is trained. You can easily self-host
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