longer answer: Random forests use the average of multiple trees that are trained in a way to reduce the correlation between trees (bagging with modified trees). Boosting trains sequentially, with each classifier working on the resulting residuals so far.
I am assuming that you meant boosted decision trees, sometimes gradient boosted decisions trees, as usually one have boosted decision trees. I think xgboost added boosted RF, and you can boost any supervised model, but it is not usual.
In theory, this means you can 'compile' most neural networks into chains of if-else statements but it's not well understood when this sort of approach works well.
I've long dismissed decision trees because they seem so ham-fisted compared to regression and distance-based clustering techniques but decision trees are undoubtedly very effective.
See more in chapter seven of the Oxford Handbook of Expertise. It's fascinating!
Given that assumption, the nebulous decision making could stem from expert's decisions being more nuanced in the granularity of the surface separating 2 distinct actions. It might be a rough technique, but nonetheless it should be able to lead to some pretty good approximations.
Decision trees predate KD trees by a decade.
Both use recursive partitioning of function domain a fundamental and an old idea.
Why "naive"? Because there is no such thing as NumPy or data frames in the Guile ecosystem to my knowledge, and the data representation is therefore probably quite inefficient.
Guile like languages are very well suited for decision trees, because manipulating and operating on trees is it's mother tongue. Only thing that would be a bit more work would be to compile the decision tree into machine code. Then one doesn't have traverse a runtime structure, the former being more efficient.
BTW take a look at Lush, you might like it.
https://news.ycombinator.com/item?id=2406325
If you are looking for vectors and tensors in Guile, there is this
having 'accessible' content is not only for people with disabilities, it also help with bad color taste.
well, at least bad taste for readable content ;)