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基于机器学习的科学中的泄漏和可重复性危机

Sayash Kapoor1, Arvind Narayanan1

  • 1Department of Computer Science and Center for Information Technology Policy, Princeton University, Princeton, NJ 08540, USA.

Patterns (New York, N.Y.)
|September 18, 2023
PubMed
概括

机器学习 (ML) 方法经常遭受数据泄露,导致过度乐观的结果. 纠正这些错误揭示了复杂的ML模型在许多科学应用中没有超过传统的逻辑回归 (LR).

关键词:
泄漏情况 泄漏情况机器学习是机器学习.可复制性的可复制性

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