LightGBM Alternatives

LightGBM reviews and mentions

• SIRUS.jl: Interpretable Machine Learning via Rule Extraction
  2 projects | /r/Julia | 29 Jun 2023

  SIRUS.jl is a pure Julia implementation of the SIRUS algorithm by Bénard et al. (2021). The algorithm is a rule-based machine learning model meaning that it is fully interpretable. The algorithm does this by firstly fitting a random forests and then converting this forest to rules. Furthermore, the algorithm is stable and achieves a predictive performance that is comparable to LightGBM, a state-of-the-art gradient boosting model created by Microsoft. Interpretability, stability, and predictive performance are described in more detail below.

• [D] RAM speeds for tabular machine learning algorithms
  1 project | /r/MachineLearning | 9 Jun 2023

  Hey, thanks everybody for your answers. I've asked around in the XGBoost and LightGBM repos and some folks there also agreed that memory speed will be a bottleneck yes.

• [P] LightGBM but lighter in another language?
  1 project | /r/MachineLearning | 4 May 2023

  LightBGM seems to have C API support, and C++ example in the main repo

• Use whatever is best for the problem, but still
  1 project | /r/datascience | 9 Aug 2022

  LGBM doesn't do RF well, but it's easy to manually bag single LGBM trees.

• What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
  3 projects | dev.to | 28 Jun 2022

  LightGBM is a 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.

• Search YouTube from the terminal written in python
  2 projects | /r/Python | 28 Feb 2022

  Microsoft lightGBM. https://github.com/microsoft/LightGBM

• LightGBM VS CXXGraph - a user suggested alternative
  2 projects | 28 Feb 2022
• Writing the fastest GBDT libary in Rust
  6 projects | dev.to | 11 Jan 2022

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

• Workstation Management With Nix Flakes: Build a Cmake C++ Package
  2 projects | dev.to | 31 Oct 2021

  { inputs = { nixpkgs = { url = "github:nixos/nixpkgs/nixos-unstable"; }; flake-utils = { url = "github:numtide/flake-utils"; }; }; outputs = { nixpkgs, flake-utils, ... }: flake-utils.lib.eachDefaultSystem (system: let pkgs = import nixpkgs { inherit system; }; lightgbm-cli = (with pkgs; stdenv.mkDerivation { pname = "lightgbm-cli"; version = "3.3.1"; src = fetchgit { url = "https://github.com/microsoft/LightGBM"; rev = "v3.3.1"; sha256 = "pBrsey0RpxxvlwSKrOJEBQp7Hd9Yzr5w5OdUuyFpgF8="; fetchSubmodules = true; }; nativeBuildInputs = [ clang cmake ]; buildPhase = "make -j $NIX_BUILD_CORES"; installPhase = '' mkdir -p $out/bin mv $TMP/LightGBM/lightgbm $out/bin ''; } ); in rec { defaultApp = flake-utils.lib.mkApp { drv = defaultPackage; }; defaultPackage = lightgbm-cli; devShell = pkgs.mkShell { buildInputs = with pkgs; [ lightgbm-cli ]; }; } ); }

• Is it possible to clean memory after using a package that has a memory leak in my python script?
  2 projects | /r/Python | 29 Apr 2021

  I'm working on the AutoML python package (Github repo). In my package, I'm using many different algorithms. One of the algorithms is LightGBM. The algorithm after the training doesn't release the memory, even if del is called and gc.collect() after. I created the issue on LightGBM GitHub -> link. Because of this leak, memory consumption is growing very fast during algorithm training.

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