I love decision trees. Conceptually simple, computational efficient and giving very good results for a lot of tasks. I especially use them on microcontroller grade system, via emlearn - which converts scikit-learn models to embedded friendly C code.
These articles are a good and pretty comprehensive introduction. I would have loved to have even more examples around the bias/variance trade off for forests, it is a key concept that not all practitioners have integrated.
I've been liking Explainable Boosting Machines lately (kind of a cross between a GAM and a tree). They also have decision trees. Haven't tested them in production yet but they're pretty to look at.
jononor•8mo ago
These articles are a good and pretty comprehensive introduction. I would have loved to have even more examples around the bias/variance trade off for forests, it is a key concept that not all practitioners have integrated.
AprilisKalends•8mo ago
mathisd•8mo ago
clockwork-dev•8mo ago
[0] https://interpret.ml/docs/ebm.html [1] https://interpret.ml/docs/dt.html