期刊
PATTERNS
卷 1, 期 8, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.patter.2020.100129
关键词
-
类别
资金
- Accelerating Creativity and Excellence (ACE) grant from Nanyang Technological University, Singapore
- NRF-NSFC [NRF2018NRF-NSFC003SB-006]
- MOE Tier 2 [MOE2019-T2-1-042]
- National Research Foundation Singapore under its AI Singapore Program [AISG-GC-2019-002, AISG-100E-2019-027, AISG-100E-2019-028]
- Kwan Im Thong Hood Cho Temple Chair Professorship
We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据