Date: 16 Oct 2020
In this article, I want to present intuition that stands behind bias-variance decomposition.
We can see the process of learning from different perspectives. In machine learning, in general, we can see learning as a process leading us to find the best hyperplane that allows us to explain our problem. In this heuristic definition there are two aspects that are key to understanding the process: “the best” and “explain our problem”. In any kind of learning, we have access only to some part of the information, so we can assume that the data we have can represent only some aspects of our problem. All this data always will be only a representation of some phenomenon, so we can intuitively feel that it will be somehow misled by different kinds of mistakes.