[bjoernbu]

Imho, it's more typical of machine learning. Sure, both can be very similar and are often signs of a lack of deeper understanding.

I'm no ML expert by any means, but I've seen several bachelor/master thesis and even ML competitions where ensembles performed best. Sure, this isn't necessarily aimless stirring and could combine models that really capture different aspects of the data. But often enough it's just several algorithms that do the same general thing, combined to achieve a slightly higher score.

Imho this is most relevant when competitions provide data that is not readable by humans (e.g. simplified: "classify these documents where all words are given as word IDs and never as actual strings").

To me this has a touch of pouring in data, stirring (build many classifiers and plug them together in an ensemble), and getting answers on the right side.

Optimizing hyper parameters goes in a similar direction, imho. I can really see an analogy to stirring