图 书 馆
XGBoost:一个可扩展的树提升系统XGBoost: A Scalable Tree Boosting System |
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| 课程网址: | http://videolectures.net/kdd2016_chen_boosting_system/ |
| 主讲教师: | Tianqi Chen |
| 开课单位: | 华盛顿大学 |
| 开课时间: | 2016-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-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. |
| 关 键 词: | 提升系统; 机器学习; 数据科学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-06-11：chenxin01 |
| 最后编审: | 2023-06-11：chenxin01 |
| 阅读次数: | 54 |