Layered TPOT：加速基于树的流水线优化
Layered TPOT: Speeding up Tree-based Pipeline Optimization
摘要：
随着对机器学习需求的增加，对更易于使用的工具的需求也在增加。已经开发了自动机器学习（AutoML）工具来解决这一需求，例如基于树的流水线优化工具（TPOT），其使用遗传编程来构建最优流水线。我们引入分层TPOT，这是对TPOT的修改，旨在创建与原始管道一样好的管道，但所用时间明显更少。这种方法根据候选管道的适应度在越来越大的数据子集上评估候选管道，使用修改的进化算法来允许在不同样本大小上训练的管道之间进行单独的竞争。经验评估表明，在足够大的数据集上，分层TPOT确实能更快地找到更好的模型。
英文摘要：
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.
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