The error here is ML terminology; the article answers correctly, if shallowly, the statistical definition of regression.
Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too".
Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.
Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too".
Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.