From my learning and project experiences, it seems that most algorithms I got exposed to are basically doing classification (actually the only regression algorithm I could think of at this point is linear regression). I am not sure why this is the case, some random assumptions include
- Classification algorithms could be extended to regression version without much efforts. Maybe there exists some mechanic way to do so, just like when people extend binary classification to multiclass version.
- Contrary to the first point, it is actually technical to do such extension and so for beginners in machine learning, there is no need to study these technical tricks.
Could someone give me some hints about this problem, thank you in advance.
I think there are some issues with this question and what is actually interesting to know is that why more classification algorithms are taught in introductory or even advanced machine learning courses than regression and other algorithms.