JPDev@programming.dev to Programmer Humor@programming.dev · 7 months agoMachine Learningprogramming.devimagemessage-square13fedilinkarrow-up1379arrow-down114
arrow-up1365arrow-down1imageMachine Learningprogramming.devJPDev@programming.dev to Programmer Humor@programming.dev · 7 months agomessage-square13fedilink
minus-squaremarcos@lemmy.worldlinkfedilinkarrow-up54·7 months agoNo, this is because the testing set can be derived from the training set. Overfitting alone can’t get you to 1.
minus-squareVictor@lemmy.worldlinkfedilinkarrow-up10·7 months agoSo as an eli5, that’s basically that you have to “ask” it stuff it has never heard before? AI has come after my time in higher education.
minus-squaremarcos@lemmy.worldlinkfedilinkarrow-up20·7 months agoYes. You train it on some data, and ask it about different data. Otherwise it just hard-codes the answers.
minus-squareArtVandelay@lemmy.worldlinkfedilinkEnglisharrow-up3·7 months agoYes, it’s called a train test split, and is often 80/20 or there about
minus-squaresevenapples@lemmygrad.mllinkfedilinkarrow-up3·7 months agoIt can if you don’t do a train-test split. But even if you consider the training set only, having zero loss is definitely a bad sign.
Is this overfitting?
No, this is because the testing set can be derived from the training set.
Overfitting alone can’t get you to 1.
So as an eli5, that’s basically that you have to “ask” it stuff it has never heard before? AI has come after my time in higher education.
Yes.
You train it on some data, and ask it about different data. Otherwise it just hard-codes the answers.
They’re just like us.
Gotcha, thank you!
Yes, it’s called a train test split, and is often 80/20 or there about
It can if you don’t do a train-test split.
But even if you consider the training set only, having zero loss is definitely a bad sign.
Gotcha!