Refer to Features to get important algorithms used in LightGBM.
| 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 |
| 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 |
LightGBM support use doxygen to generate documents for classes and functions.
Refere to the comments in c_api.h.
Follow the implementation of python-package.
Feel free to open issues if you met problems.