Development Guide

Algorithms

Refer to Features to get important algorithms used in LightGBM.

Classes And Code Structure

Important Classes

Class	description
Application	The entrance of application, including training and prediction logic
Bin	Data structure used for store feature discrete values(converted from float values)
Boosting	Boosting interface, current implementation is GBDT and DART
Config	Store parameters and configurations
Dataset	Store information of dataset
DatasetLoader	Used to construct dataset
Feature	Store One column feature
Metric	Evaluation metrics
Network	Newwork interfaces and communication algorithms
ObjectiveFunction	Objective function used to train
Tree	Store information of tree model
TreeLearner	Used to learn trees

Code Structure

Path	description
./include	header files
./include/utils	some common functions
./src/application	Implementations of training and prediction logic
./src/boosting	Implementations of Boosting
./src/io	Implementations of IO relatived classes, including Bin, Config, Dataset, DatasetLoader, Feature and Tree
./src/metric	Implementations of metrics
./src/network	Implementations of network functions
./src/objective	Implementations of objective functions
./src/treelearner	Implementations of tree learners

API Documents

LightGBM support use doxygen to generate documents for classes and functions.

C API

Refere to the comments in c_api.h.

High level Language package

Follow the implementation of python-package.

Ask Questions

Feel free to open issues if you met problems.