Re:Translating Between Statistics and Machine Learning
by wodenokoto on Tuesday November 20, 2018 @04:40AM
(#18492740)
Yes, in ML (and I believe for regular statistics as well) regression is when you are trying to predict a number rather than a label. Predicting house prices is a classical example.
An exception for this is "logistic regression" which is accepted by both the stats and ML communities.
Supervised learning is whenever you have a target variable for the observations you use to train/fit your model.
If you only have targets for some of your observations, it is called "semi-supervised learning". Although in deep learning you often talk about "pre-training" your model, which often is adjusting weights in an unsupervised way.
So you can have supervised regression as well as supervised label predictors. These can also be semi-supervised.
vs.
> ML: supervised learners, machines
I think ML uses the term regression for the situations where the output is numeric value (as opposed to a label), and supervised learning is more than just regression. Usually regression models have mean squared error loss function, that's one way to spot them.