xgboost-0.82-py2.py3-none-win_amd64.whl

陈天奇xgb论文。Tree boosting is a highly eective and widely used machine learning method. In this paper, we describe a scalable endto- end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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二分类数据集，乳腺癌数据集，568条记录，30类特征，可以用分类算法如xgboost，svm等分类算法

大数据算法可能使用的库，主要是给那些不方便下载的人免费的途径。

比较偏重于理论阐述的工具书，原理解释、数学推导比较详细，可参考论文一起看。

机器学习算法XGboost、LightGBM、Catboost的代码架构，满足基本的数据分析，回归、二分类、多分类。

xgboost的安装包。使用方法：将文件放入python文件下的Scripts文件夹下，打开cmd，到Scripts目录下输入：pip installer xgboost.dll，等待安装完成即可。

安装xgboost时出现错误是可能是缺少了相应的xgboost.dll工具包所致，这个包能完美解决安装时出现的错误。

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.