lleaves VS catboost

I built this decision tree (LightGBM) compiler last summer: https://github.com/siboehm/lleaves

It get's you ~10x speedups for batch predictions, more if your model is big. It's not complicated, it ended up being <1K lines of Python code. I heard a couple of stories like yours, where people had multi-node spark clusters running LightGBM, and it always amused me because by if you compiled the trees instead you could get rid of the whole cluster.

In a near-future version I'll expose some of the compilation parameters, I was somewhat afraid of having an API that's too complicated deterring people who just want a no-fuzz drop-in replacement for LightGBM. But as long as I keep sane defaults and have the parameters optional it should be fine. Relevant parameters are definitely block size (needs to adjust to L1i size and tree size) as well as the LLVM codemodel (a smaller adress space increases single-batch prediction speeds but doesn't work for large models). The thread-size specific compilation I'm still looking into, it makes the API more complicated and so might not be worth it.

CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.

Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/

CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.

If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.

The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.