I'm aware of and have worked with many datasets in Classical ML as well as DL. I am also aware of some of the standard datasets in DL (for example ImageNet for Image Classification, etc.)
However, I was wondering if there are any standard datasets (or benchmarks for accuracy) for the Classical methods such as Regression, GBM, SVM, etc. More specifically, are there any standard datasets that can be used to measure the accuracy of a new method?
Given that most of the Classical methods are very old, the datasets they would've used to test their methods may not be relevant today.
If there are no such standards, can you comment on the class of applications you would like to see if someone were to create their own standard dataset?