图 书 馆
学习可证明的分类数据最优规则列表Learning Certifiably Optimal Rule Lists for Categorical Data |
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| 课程网址: | http://videolectures.net/kdd2017_angelino_categorical_data/ |
| 主讲教师: | Elaine Angelino |
| 开课单位: | 加州大学伯克利分校 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. This framework is a novel alternative to CART and other decision tree methods. |
| 关 键 词: | 分类数据; 算法边界; 数据科学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-12-26：wujk |
| 最后编审: | 2024-01-25：liyy |
| 阅读次数: | 19 |