Decision trees – the unreasonable power of nested decision rules
mlu-explain.github.io
Decision trees – the unreasonable power of nested decision rules
1–10 of 89 posts
Re: Decision trees – the unreasonable power of nested decision rules
#2Interesting website and great presentation. My only note is that the color contrast of some of the text makes it hard to read.
Re: Decision trees – the unreasonable power of nested decision rules
#3[dead]
Re: Decision trees – the unreasonable power of nested decision rules
#4Interesting website and great presentation. My only note is that the color contrast of some of the text makes it hard to read.
exactly my thought. and here thr reader view of FF is a godsend.
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 ;)
Re: Decision trees – the unreasonable power of nested decision rules
#5That was beautifully presented!
Re: Decision trees – the unreasonable power of nested decision rules
#6Fun 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.
Re: Decision trees – the unreasonable power of nested decision rules
#7Fun 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.
Do you know of any software that does this? Or any papers on the matter? It could be a fun weekend project
Re: Decision trees – the unreasonable power of nested decision rules
#8Fun 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.
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Re: Decision trees – the unreasonable power of nested decision rules
#9Decision 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.
Re: Decision trees – the unreasonable power of nested decision rules
#10Experts' 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.
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!