Welcome Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects. Gradient Boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions. This is my goal for you and this book is your ticket to that outcome.

win10 x64安装xgboost错误提示所缺少的dll。 下载放到错误提示的目录下，重新安装即可。

windows 系统下载后 直接在cmd下 pip install xgboost目录下的这个whl文件即可安装

利用xgboost训练印第安人糖尿病数据集，包括准确率，特征重要程度的相关代码

spark版本xgboost的jar包，博客中有scala-spark使用案例

用forecastxgb做预测分析

xgboost-whl安装包是可以很方便的在windows7和windows10下安装修改版xgboost的包文件

xgboost是陈天奇大牛新开发的Boosting库。它是一个大规模、分布式的通用Gradient Boosting（GBDT）库，它在Gradient Boosting框架下实现了GBDT和一些广义的线性机器学习算法。

根据我的课程设计写了一个xgboost代码，效果不错。希望能帮助跟我一样的初学者。

陈天奇论文及翻译+演讲PPT+论文讲解（陈天奇XGBoost: A Scalable Tree Boosting System）