论文标题
拉尔：一致的自动化机器学习
Lale: Consistent Automated Machine Learning
论文作者
论文摘要
自动化的机器学习使数据科学家更容易通过搜索可能的选择超参数，算法甚至管道拓扑来开发管道。不幸的是，自动化机器学习工具的语法与手动机器学习，彼此以及错误检查的语法不一致。此外，很少有工具支持高级功能，例如拓扑搜索或高阶操作员。本文介绍了Lale，Lale是一个高级Python接口的库，以一致的方式简化和统一了自动化的机器学习。
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.
