(ML 11.5) Bias-Variance decomposition

Explanation and proof of the bias-variance decomposition (a.k.a. bias-variance trade-off) for estimators.

[@Tyokok]

Thanks for the great video! 3 question, 1) could you please explain a bit more what is theta here? Is it predict function or just params of predict function? 2) and what does it exactly mean conditional on give theta? 3) 4:30 what is this mu= expectation of theta hat doing expectation over of? Many Thanks!

[@yazhouhao7086]

I think \theat is not a constant, it's just deterministic.

[@youtubecommenter5122]

I'm just here to read all the Asians commenting

[@tumul1474]

Thanks a bunch sir you just saved my ass !!

[@plttji2615]

Thank you for the video, can you help me how to prove that is unbiased in this question? Question: Compare the average height of employees in Google with the average height in the United States, do you think it is an unbiased estimate? If not, how to prove it is not matched?

[@Twilightsfavquill]

Very well explained. Thanks!

[@nprasenreddy]

neat

[@ChoponHill]

dang! this is tight.

[@tz7380]

great video!

[@basantmounir]

Elegant. ❤

[@tymothylim6550]

Thank you very much for this video :) I learnt something from it :)

[@alexanderyau6347]

neat and elegent

[@fzhcary]

nice tutorial. a small error at 8': the first term should be bias and 2nd term is variance.

[@alexanderschiendorfer2203]

No, I think the terms are correct. It is aligned with his explanations.