# Classification of a single group?

This might be completely unfeasible or obvious, but I wanted to see if anyone has come across a similar problem. Typical classification methods (decision tree, random forest, etc) require at least two groups to determine group differences, however is it possible to have a singular group, and classify it based on the probability that it's derived from that group or not?

I guess the comparison that comes to mind is a shapiro-Wilk test which determines if a vector is derived from the normal distribution. Is there a method or modelling technique in which you could take a set of variables that characterise a single group, and perform a test to give the probability that it's derived from that group?

Thanks, and apologies if this is really obvious.

• As noted by others, there are such methods designed for outlier detection, anomaly detection, or novelty detection, e.g. one-class SVM. – Tim Jan 19 '17 at 17:11

There is a good section in the scikit-learn documentation that you can start with and may lead you to other interesting sources.