对混合-自动驾驶系统的最后评述和进一步工作

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引用次数: 0

摘要

本章指出,各种用例已经证明,本研究的目的和贡献是概念化、设计和开发一个可扩展和灵活的工具,用于在单个或多个变化的数据集上进行自动大数据ML模式和模型选择。hybrid-autoML工具箱的一个主要优点是,它减少了数据科学家和研究人员在算法选择和超参数空间中搜索的时间。在5.2节中讨论了这一优势,作者将混合automl工具与autoWeka在大约35个数据集上使用精度、平均绝对误差(MAE)和时间等指标进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Final Remarks and Further Work for the Hybrid-AutoML System
This chapter addresses that the various use cases have proved that the aims and contributions of this research to conceptualise, design, and develop a scalable and flexible toolkit for automatic big data ML mode and model selection, on single or multi-varying datasets has been achieved. A major benefit of the hybrid-autoML toolkit is that it reduces the time data scientists and researchers in the field spend, searching through the algorithm selections and hyper parameter space. This advantage was discussed in Section 5.2 where the authors compared the hybrid-autoML tool with autoWeka on about 35 datasets using measures such as accuracy, mean absolute error (MAE), and time.
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