官方代码结构解析，README.MD 

Coding Guide
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This file is intended to be notes about code structure in xgboost

Project Logical Layout // 依赖关系，IO -> LEANER（计算梯度并且传导给GBM）-> GBM（梯度提升） -> TREE(构建树的算法)  
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* Dependency order: io->learner->gbm->tree
  - All module depends on data.h
* tree are implementations of tree construction algorithms.
* gbm is gradient boosting interface, that takes trees and other base learner to do boosting.
  - gbm only takes gradient as sufficient statistics, it does not compute the gradient.
* learner is learning module that computes gradient for specific object, and pass it to GBM

File Naming Convention // .h定义数据结构和接口，.hpp实现接口，每个文件夹下面，都有一个.cpp 用来编译该模块
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* .h files are data structures and interface, which are needed to use functions in that layer.
* -inl.hpp files are implementations of interface, like cpp file in most project.
  - You only need to understand the interface file to understand the usage of that layer
* In each folder, there can be a .cpp file, that compiles the module of that layer

How to Hack the Code // 目标函数定义和修改
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* Add objective function: add to learner/objective-inl.hpp and register it in learner/objective.h ```CreateObjFunction``` 
  - You can also directly do it in python
* Add new evaluation metric: add to learner/evaluation-inl.hpp and register it in learner/evaluation.h ```CreateEvaluator``` 
* Add wrapper for a new language, most likely you can do it by taking the functions in python/xgboost_wrapper.h, which is purely C based, and call these C functions to use xgboost

　　

XGBoost: eXtreme Gradient Boosting 

An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also also distributed and scale to Terascale data.