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lightgbm-org/lightgbm

📈 Trending★ 18,360C++Audience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Fast, memory-efficient machine learning framework that combines many decision trees to make accurate predictions on large datasets.

Mindmap

mindmap
  root((LightGBM))
    What it does
      Gradient boosting
      Decision tree ensemble
      Classification and ranking
    Key features
      Fast and memory efficient
      Parallel training
      GPU acceleration
    Supported languages
      Python
      R
      C
    Use cases
      Competition winning models
      Large dataset prediction
      Hyperparameter tuning

Things people build with this

USE CASE 1

Build a classification model to predict customer churn or fraud on millions of records.

USE CASE 2

Train a ranking model to personalize search results or recommendations using GPU acceleration.

USE CASE 3

Win machine learning competitions by combining LightGBM with automated hyperparameter tuning.

USE CASE 4

Process datasets too large for memory by using LightGBM's efficient tree-building algorithm.

Tech stack

C++PythonRCUDAOpenMP

Getting it running

Difficulty · moderate Time to first run · 30min

Requires compilation from C++ source; pre-built wheels may not be available for all platforms.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

LightGBM is a machine learning framework written in C++ that implements gradient boosting, a technique where many simple decision trees are combined in sequence, each one learning to correct the mistakes of the previous ones, to produce accurate predictions for tasks like classifying data or ranking items. The framework is designed to be faster and use less memory than comparable tools, while maintaining or improving accuracy. It supports training in parallel across multiple CPU cores or machines, and can also use graphics processing units to accelerate computation. It handles large datasets that would be impractical for some other approaches. LightGBM has official interfaces for Python, R, and C, and the community has created additional bindings for other languages. It has been used in many machine learning competition winning solutions. The readme notes the project moved from the Microsoft GitHub organization to its own organization in March 2026 but remains managed by the same team. The project includes extensive documentation covering installation, available parameters, distributed training, and integration with automated hyperparameter tuning tools. It is licensed under the MIT license.

Copy-paste prompts

Prompt 1
Show me how to install LightGBM in Python and train a basic classifier on a CSV file.
Prompt 2
How do I set up distributed training across multiple machines with LightGBM?
Prompt 3
What are the key hyperparameters I should tune in LightGBM for a regression problem?
Prompt 4
How do I use GPU acceleration with LightGBM to speed up training on a large dataset?
Prompt 5
Show me an example of integrating LightGBM with a hyperparameter tuning tool like Optuna.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.