I'm studying Probably approximately correct learning, and I don't understand what an Instance Space and a Concept is.
I have see that wikipedia https://en.wikipedia.org/wiki/Probably_approximately_correct_learning provides various examples, but it's still rather an abstract concept.
Could you provide me with an intuitive definition and some tangible examples?
Topic pac-learning
Category Data Science
This kind of language is typically associated within a field of math called computational learning theory (clt). CLT is inherently abstract since it's trying to derive general observation about unknown target functions. That's why you get generic terms like instance space and concepts. Broadly speaking an instance space is typically just the domain of X (your sample population) and an instance of that space is a particular x with various features. For example, X may be the set of all people and an instance of x will be an individual person who is 30 female and medium height etc.
The concept space on the other hand is subset of the domain X such that X -> {0, 1} or in other words a subset that maps X via Boolean function as either 0 or 1. For example, Our concept space can be people who walk to work. Every person X can either say yes they walk to work (and thus x -> 1) or no (x -> 0).
If this is still too abstract for you, I would recommend stepping back and building a more foundation footing on basic machine learning before you start trying to study them as broad generic objects.
Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.