The distinction between classification and regression accounts for a model output. I know that classification models have discrete and regression continuous outputs.
I want to focus on a taxonomy subtlety between these classifications and regression tasks, though that has been bothering me and I have encountered in many places, for example:
In some cases, classification algorithms will output continuous values in the form of probabilities. Likewise, regression algorithms can sometimes output discrete values in the form of integers.
Maybe the source is not the best example, but this is not the first article I see such a statement in.
Classification with probabilities output seems odd because: probabilities is any number in the continuous interval [0,1]. Can someone elaborate please and mention a specific model?
IMHO, many models can be reformed for either, but a task and model are different things.
An example that might fit the case, I think, but I am not sure is valid are neural networks with softmax outer nodes which have continuous output in [0,1], but we select the maximum among the nodes in a classification task. Otherwise, we speak of a generative model (for example, the decoder part of VAE).
Is there some formal definition that incorporates that or should I just stop reading random articles?