KnowledgePit与BrightBox相遇:对数据科学竞赛结果进行深入调查的一步

Andrzej Janusz, D. Ślęzak
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引用次数: 6

摘要

我们讨论了将KnowledgePit数据科学竞赛平台与BrightBox技术集成的好处,该技术旨在诊断嵌入复杂软件系统中的机器学习模型。我们简要回顾一下在KnowledgePit举办的国际挑战的历史,并讨论在何种意义上像BrightBox这样的技术可以在挑战后分析中提供帮助。特别是,我们展示了如何结合参赛者提交的解决方案,以获得更准确的预测。所讨论的功能对于数据科学/机器学习在线竞赛的赞助商和组织者来说非常重要,因为它们支持在设计现实世界问题的最终解决方案时采用提交的内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions
We discuss the benefits of integrating the KnowledgePit data science competition platform with the BrightBox technology aimed at diagnostics of machine learning models embedded within complex software systems. We briefly recall the history of international challenges held at KnowledgePit and we also discuss in what sense such technologies as BrightBox can be helpful during the post-challenge analysis. In particular, we show how to combine solutions submitted by the competition participants in order to obtain even more accurate predictions. The discussed functionalities are of significant importance for the sponsors and organizers of data science / machine learning online contests because they support adoption of submissions while designing ultimate solutions of real-world problems.
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