I would like to ask, as the title suggest, if anyone has a general implementation of boosting algorithm? I did some search and found that it is usually tightly coupled with a base learners being decision trees. But Boosting is a general meta-algorithm which assumes that the underlying base learner can fit possibly weighted samples and perform prediction on them.
I have recently started to wonder, why general people believes that NNs sucks on tabular datasets and Boosted decision trees shines. I came to conclusion that boosting might be the culprit, since single tree sucks as well. Since I would like to know, if I am right or wrong, I would like to test (and also would like to be right).
Due to my lack of time (you can also read it laziness), I would like to ideally hook already existing implementation (my six years old implementation is in matlab).
Well, thanks for answers and opinions on the matter of learning tabular data.