Machine learning can be divided into several areas: supervised learning, unsupervised learning, semi-supervised learning, learning to rank, recommendation systems, etc, etc.

One such area is [PU Learning](https://en.wikipedia.org/wiki/One-class_classification), where only Positive and Unlabeled instances are available.

There are many publications about this, usually involving a lot of mathematics...

When looking at the literature, I was expecting to see methods similar to [self-training](https://www.sciencedirect.com/science/article/pii/S0925231217309608) (from semi-supervised learning), where labels are adjusted gradually according to the classifier margins.

I don't think these is what practitioners from the area do, and I was unable to navigate the mathematics or to find a survey on PU learning.

Could someone from the area perhaps clarify what said practitioners do? Why can they not just use a binary classifier where the negative class=unlabeled? Can negative labels exist among the unlabeled data? What is the goal and what metrics exist to evaluate said goal?