Experts' nebulous decision making can often be modelled with simple decision trees and even decision chains (linked lists). Even when the expert thinks their decision making is more complex, a simple decision tree better models the expert's decision than the rules proposed by the experts themselves. See more in chapter seven of the Oxford Handbook of Expertise. It's fascinating!
Decision trees are great. My favorite classical machine learning algorithm or group of algorithms, as there are many slight variations of decision trees. I wrote a purely functional (kind of naive) parallelized implementation in GNU Guile: https://codeberg.org/ZelphirKaltstahl/guile-ml/src/commit/25...
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.
Fun fact - single bit neural networks are decision trees.
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.
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.
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.
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 ;)