thinking abt once when a discussion abt bias in ML came up and an ML engineer was like "i don't see how there's a problem with ML or the model, it's just an issue with the training data"https://twitter.com/nicolaskb/status/1244921742486917120 …
-
I just mean that whenever a supervised learning model forecast disagrees with what a training label would hypothetically be then it's a model error, because it's learning an association of "dark skin->guns" that doesn't generalize outside the training set.
yeah i see how it's distinct from the amazon example below but i'd argue it's still a training set issue. can't say if the model is overfitting here — maybe it's learning everything it's fed at the correct level but w/o data bias we wouldn't have told it to learn this at all
-
-
either way models and their training data are inextricable from each other and u gotta check both to make sure they make sense
-
This Tweet is unavailable.
- 1 more reply
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.