Development Guide


Refer to Features to understand 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 storing feature discrete values (converted from float values)
Boosting Boosting interface (GBDT, DART, GOSS, etc.)
Config Stores parameters and configurations
Dataset Stores information of dataset
DatasetLoader Used to construct dataset
Feature Stores one column feature
Metric Evaluation metrics
Network Network interfaces and communication algorithms
ObjectiveFunction Objective functions used to train
Tree Stores 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

Documents API

Refer to docs README.


Refere to the comments in c_api.h.

High Level Language Package

See the implementations at Python-package and R-package.


Refer to FAQ.

Also feel free to open issues if you met problems.