Welcome to LightGBM’s documentation! LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: …

Features This is a conceptual overview of how LightGBM works [1]. We assume familiarity with decision tree boosting algorithms to focus instead on aspects of LightGBM that may differ from other boosting …

LightGBM can use categorical features as input directly. It doesn’t need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).

Python API Data Structure API Training API

Quick Start This is a quick start guide for LightGBM CLI version. Follow the Installation Guide to install LightGBM first. List of other helpful links Parameters Parameters Tuning Python-package Quick Start …

GPU Tuning Guide and Performance Comparison How It Works? In LightGBM, the main computation cost during training is building the feature histograms. We use an efficient algorithm on GPU to …

Installation Guide Versioning LightGBM releases use a 3-part version number, with this format:

LightGBM GPU Tutorial The purpose of this document is to give you a quick step-by-step tutorial on GPU training. We will use the GPU instance on Microsoft Azure cloud computing platform for …

Parameters Tuning This page contains parameters tuning guides for different scenarios. List of other helpful links Parameters Python API FLAML for automated hyperparameter tuning Optuna for …

see lightgbm-transform for usage examples Note: lightgbm-transform is not maintained by LightGBM’s maintainers. Bug reports or feature requests should go to issues page New in version 4.0.0 Predict …