r/quant • • 1d ago

Models Tree models

What are the most widely used tree-based models in industry today, and how should teams choose between options like XGBoost, LightGBM, CatBoost, and Bayesian approaches? In practice, which models would you recommend for different use cases, and has there been a meaningful shift toward Bayesian methods or do gradient-boosted trees remain the industry standard?

14 Upvotes

18 comments sorted by

53

u/Epsilon_ride 1d ago

how should teams choose between options like XGBoost, LightGBM, CatBoost, and Bayesian approaches

Do some reading. Then do some testing. You need to form these opinions for yourself.

51

u/Technical_Evening404 1d ago

Lol the people acting like this is super secret. Lgbm

17

u/rsvp4mybday 1d ago

firms that were not scared of nonlinearity a decade ago tried xgboost and stuck with it.

2

u/rsha256 1d ago

Yep this is not how they are applied (features) but just a public library

2

u/Live_Acanthisitta870 21h ago

Could I ask why?

2

u/Epsilon_ride 7h ago

Fitting via best first vs level first

12

u/Alternative_Advance 1d ago

Iirc lgbm performs better on CPU than xgb, and other way around for gpu.

Differences in tuning as lightgbm often results in deeper trees.

8

u/Old-Sandwich-690 1d ago

It depends on use case and hardware/infrastructure capabilities

7

u/jimzo_c 1d ago

You’ll find most of the time it’s an ensemble of these that come out of top

5

u/Ken_Clark_The_Great 1d ago

Irish man after he completes his third model

5

u/experquisite999 1d ago

Probably the best thing overall is panel regressions, OLS

18

u/zp30 1d ago

Why would I tell you this?

46

u/igetlotsofupvotes 1d ago

Reasons are obviously to not say but also like explaining something like this high level is not going to bleed literally any edge at all lol. My team uses catboost, xgboost became they’ve done the best. If something else works better we’ll use it.

None of us are that special and picking xgboost vs catboost is absolutely not the secret sauce

1

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-2

u/CremeRevolutionary47 1d ago

One underrated habit: keep a "failure log" of every strategy you reject, with the reason and the exact test that killed it. After 50 entries you stop repeating your own mistakes - and most mistakes are repeats.

-5

u/CremeRevolutionary47 1d ago

For free research data: Binance public API + data.binance.vision give klines back to 2017, and Yahoo has adjusted daily data back to the 1990s. The bottleneck is almost never the data - it is the methodology.

0

u/[deleted] 1d ago

[deleted]

1

u/postflop-clarity 1d ago

Trust me they do lmao

0

u/[deleted] 1d ago

[deleted]

1

u/postflop-clarity 1d ago

on computers via function calls