Before moving on, there are two really important concepts to be covered for predictive analytics. Errors can be divided into the following two types:
- Reducible errors: These errors can be reduced by making certain improvements to the model
- Irreducible errors: These errors cannot be reduced at all
Let's assume that, in machine learning, there is a relationship between features and target that is represented with a function, as shown in the following screenshot:
Let’s assume that the target (y) is the underlying supposition of machine learning, and the relationship between the features and the target is given by a function. Since, in most cases we consider that there is some randomness in the relationship between features and target, we add a noise term here, which will always be present in reality. This is the underlying supposition...
