http://www.deeplearningbook.org/contents/ml.html Page 116 explains bayes error as below
The ideal model is an oracle that simply knows the true probability distribution that generates the data. Even such a model will still incur some error on many problems, because there may still be some noise in the distribution. In the case of supervised learning, the mapping from x to y may be inherently stochastic, or y may be a deterministic function that involves other variables besides those included in x. The error incurred by an oracle making predictions from the true distribution p(x, y) is called the Bayes error.
- Please explain Bayes error intuitively?
- How is it different from irreducible error?
- Can I say total error = Bias + Variance +Bayes error?
- What is meaning of "y may be inherently stochastic"?