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General framework for cross-validation of machine learning algorithms using SQL on distributed systems
General framework for cross-validation of machine learning algorithms using SQL on distributed systems
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机译:在分布式系统上使用SQL对机器学习算法进行交叉验证的通用框架
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摘要
A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.
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