Understanding Irreducible Error and Bias (By Emily Fox)

Okay, so, we've talked about three different measures of error. And now in this part, we're gonna talk about three different sources of error. And this is gonna lead us into a conversation of the bias variance trade-off. Okay, so when we were forming our prediction, there are three different sources of error. Noise, bias, and variance. And in this part, we're gonna walk through these three different components, at a very high level. At a more intuitive level. And then following this, there are gonna be two optional sections that go into much more formalism and detail about this. But those are optional because we're not requiring that you know this to get through the course. But for those that are interested, we will be providing the formalism behind these notions that I'm presenting now. Let's look at this first term, this noise term. And as we've mentioned many times in this specialization, data are inherently noisy.

[@kofiasante9477]

Is bias and variance what we refer to as reducible error?

[@MachineLearningTV]

Yes