That’s correct

I’ve spent most of the last 20 years doing reinforcement learning and it is exceptionally simple conceptually

The challenge is data acquisition and the right types of process frameworks

This varies so wildly from domain to domain. Highly structured data (time series, photos, audio, etc.) typically has a metric boatload of feature extraction methodology. Neural networks often draw on and exploit that structure (i.e. convolutions). You could even get some pretty good results on manually-extracted neural-network-esque features handed off to a random forest. This heuristic begins to fall off with deep learning though, which imo, is a precursor to LLMs and showed that emergent complexity is possible with machine learning.

But non-structured data? Pretty pointless to hand off to a neural network imo.

That has not been my experience though, apart from the need of standard data hygiene that one has to maintain for any ML exercise.

Normalising the numeric features to a common range has been adequate. This too is strictly not necessary for DTs, but pretty much mandatory for linear models. (DTs are very robust to scale differences among features, linear models quite vulnerable to the same.)

One can think of each tree path from root to leaf as a data driven formulation/synthesis of a higher level feature built out of logical conjunctions ('AND' operation).

These auto-synthesized / discovered features are then ORed at the top. DTs are good at capturing multi-feature interactions that single layer linear models can't.

NNs certainly synthesize higher level features, but what does not get highlighted enough is that learning-theory motivated Adaboost algorithm and it's derivatives do that too.

Their modus operandi is "BYOWC, bring your own weak classifier, I will reweigh the data in such a way that your unmodified classifier will discover a higher level feature on its own. Later you can combine these higher features linearly, by voting, or by averaging".

I personally favor differentiable models over trees, but have to give credit where it's due, DTs work great.

What leaves me intellectually unsatisfied about decision trees is that the space of trees cannot be searched in any nice principled way.

Or to describe in terms of feature discovery, in DT, there's no notion of progressing smoothly towards better high level features that track the end goal at every step of this synthesis (in fact greedy hill climbing hurts performance in the case of decision trees). DTs use a combinatorial search over the feature space partitioning, essentially by trial and error.

Neural nets have a smooth way of devising these higher level features informed by the end goal. Roll infinitesimally in the direction of steepest progress -- that's so much more satisfying than trial and error.

As for classification performance where DT have struggled are cases where columns are sparse (low entropy to begin with).

Another weakness of DT is the difficulty of achieving high throughput runtime performance, unless the DT is compiled into machine code. Walking a runtime tree structure with not so predictable branching probabilities that doe'snt run very fast on our standard machines. Compared to DNNs though this is a laughable complaint, throughput of DTs are an order of one or two faster.

Yes.

I mention restricted oblique trees in passing in my original comment. In my experience, oblique trees tend to add considerable complexity, the others more so. Of course whether the complexity is warranted or not will depend on the dataset.

The merit of what I used is in its simplicity. Any simple ML library would have a linear classifier and a tree learner.

Super easy to implement, train, maintain, debug. One to two person team can handle this fine.

I missed a word "recursively", that I have edited in my original comment now.

Consider connected regions in the domain that have the same label. Much like countries on a political map. The situation where this has a short description in terms of recursive subdivision of space, is what I am calling a partitioned structure. It's really rather tautological.

Oh theirs is a far more sophisticated and deeper idea, to look for invariant representations.

Mine is a quick but effective practical hack fuelled by a little bit of insight.

I completely agree, as you may infer from my comment. The second multivariate models are relevant we effectively trade explainability for discrimination power. If your decision tree/model needs to be large enough to warrant SGD or similar optimization techniques, it is pretty much a fantasy to ever analyze it formally.

My second job after physics was AI for defense, and boy is the dream of explainable AI alive there.

Honesty anyone who “needs” AI to be understandable by dissection, suffers from control issues :)

If you sum up experimental physics into one heuristic it is “avoid fooling yourself with assumptions” - I left physics over a decade ago, but I feel confident that physicists still work hard to understand what they observe and don’t let LLMs have all the fun. If there’s one field of science where the scientists are legitimately allowed to go all the way back to basics, it’s elementary particle physics.

short answer: No.

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.

The training process differs, but the resulting model only differs in data rather than code -- you evaluate a bunch of trees and add them up.

For better or for worse (usually for better), boosted decision trees work harder to optimize the tree structure for a given problem. Random forests rely on enough trees being good enough.

Ignoring tree split selection, one technique people sometimes do makes the two techniques more related -- in gradient boosting, once the splits are chosen it's a sparse linear algebra problem to optimize the weights/leaves (iterative if your error is not MSE). That step would unify some part of the training between the two model types.

Closest thing I found was:

Single Bit Neural Nets Did Not Work - https://fpga.mit.edu/videos/2023/team04/report.pdf

> We originally planned to make and train a neural network with single bit activations, weights, and gradients, but unfortunately the neural network did not train very well. We were left with a peculiar looking CPU that we tried adapting to mine bitcoin and run Brainfuck.

> I still don't know exactly what you mean

Straight forward quantization, just to one bit instead of 8 or 16 or 32. Training a one bit neural network from scratch is apparently an unsolved problem though.

> The trees that correspond to the neural networks are huge.

Yes, if the task is inherently 'fuzzy'. Many neural networks are effectively large decision trees in disguise and those are the ones which have potential with this kind of approach.

Made me think of https://github.com/xoreaxeaxeax/movfuscator. Would be definitely cool to see it realized even if it would be incredibly impractical (probably).

I think any quantization approach should work, not an expert on this.

I think data frames are quite memory efficient and can store non-uniform data types (as can vectors in Guile). Generally, a ton of work has gone into making operations on data frames fast. I don't think a normal vector or multi-dimensional array can easily compete. Data frames are probably also compiled to some quite efficient machine code. Not sure whether Guile's native data structures can match that. Maybe they can.

Also I think I did not optimize for memory usage, and my implementation might keep copies of subsets of data points for each branch. I was mostly focused on the algorithm, not that much on data representation.

Another point, that is not really efficiency related, is that data frames come with lots of functionality to handle non-numeric data. If I recall correctly, they have functionality like doing one-hot encoding and such things. My implementation simply assumes all you have is numbers.

There might also be efficiency left on the table in my implementation, because I use the native number types of Guile, which allow for arbitrarily large integers (which one might not need in many cases) and I might even have used fractions, instead of inexact floats.

I guess though, with good, suitable data structures and a bit of reworking the implementation, one could get a production ready thing out of my naive implementation, that is even trivially parallelized and still would have the linear speedup (within some bounds only, probably, because decision trees usually shouldn't be too deep, to avoid overfitting) that my purely functional implementation enables.

Thanks for the links!

tree algorithms on sklearn use parallel arrays to represent the tree structure.

You have this thing a little backwards that it is unintentionally hilarious.

Decision trees predate KD trees by a decade.

Both use recursive partitioning of function domain a fundamental and an old idea.

in the interest of understanding, is there any code or similar for the approach? does that OCR run anywhere today?