Development Guide

Algorithms

Refer to Features for understanding of important algorithms used in LightGBM.

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

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 related 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

Refer to C API or the comments in c_api.h file, from which the documentation is generated.

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

Refer to FAQ.

Also feel free to open issues if you met problems.