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Avoiding common pitfalls in machine learning omic data science
Nature Materials ( IF 41.2 ) Pub Date : 2018-11-26 , DOI: 10.1038/s41563-018-0241-z Andrew E. Teschendorff
Nature Materials ( IF 41.2 ) Pub Date : 2018-11-26 , DOI: 10.1038/s41563-018-0241-z Andrew E. Teschendorff
Avoiding common pitfalls in machine learning omic data science
中文翻译:
避免机器学习omic数据科学中的常见陷阱
避免机器学习omic数据科学中的常见陷阱
更新日期:2019-01-25
Avoiding common pitfalls in machine learning omic data science, Published online: 26 November 2018; doi:10.1038/s41563-018-0241-z
This Comment describes some of the common pitfalls encountered in deriving and validating predictive statistical models from high-dimensional data. It offers a fresh perspective on some key statistical issues, providing some guidelines to avoid pitfalls, and to help unfamiliar readers better assess the reliability and significance of their results.中文翻译:
避免机器学习omic数据科学中的常见陷阱
避免机器学习omic数据科学中的常见陷阱
避免机器学习omic数据科学中的常见陷阱,在线发布:2018年11月26日; doi:10.1038 / s41563-018-0241-z
该评论描述了从高维数据推导和验证预测统计模型时遇到的一些常见陷阱。它为一些关键的统计问题提供了新的视角,并提供了一些指南来避免陷阱,并帮助不熟悉的读者更好地评估结果的可靠性和重要性。