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.
Questions
- 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"?