- Python 程式设计经验
- 机器学习演算法知识
观众
- 开发人员
Skope-rules 是一个构建在 scikit-learn 之上的 Python 机器学习模组。
在这个讲师指导的实时培训（现场或远端）中，参与者将学习如何使用 Python skope-rules 根据现有数据集自动生成规则。
在本次培训结束时，参与者将能够：
- 使用 skope-rules 从可用资料中提取规则。
- 应用 skope-rules 来执行分类，这在监督式异常检测或不平衡分类中特别有用。
- 生成用于对新传入数据进行分类的规则。
- 调整规则以解决欺诈检测、预测性维护、入侵检测、保险申请审批等方面的实际问题。
课程形式
- 在现场实验室环境中进行部分讲座、部分讨论、练习和大量动手实践。
注意
- 要申请本课程的定制培训，请联系我们进行安排。
- 要了解有关 skope-rules 的更多资讯，请访问：https://github.com/scikit-learn-contrib/skope-rules
介绍
- 为什么要从数据中提取规则？
Sklearn 模组概述（决策树/随机 Forrest）
安装和配置 skope-rules
案例研究：检测信用违约率
汇入数据
使用 SkopeRules 进行不平衡分类
训练 SkopeRules 分类器
提取规则
融合规则
将分类树和回归树拟合到子样本
选择更高精度的规则
测试更高精度的规则
总结和结论
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