Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, even if given unlimited training time, is undecidable. We discuss the importance of this result and potential topics for further research, including information-theoretic and probabilistic strategies for bounding learning algorithm fit.

中文翻译：

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学习算法欠拟合的不确定性

使用最新的机器学习结果，该研究提出了信息理论上关于欠拟合和过度拟合的观点，我们证明，确定可编码的学习算法是否即使在给定培训时间不受限制的情况下也始终会拟合数据集是不确定的。我们讨论了此结果的重要性以及可能进行进一步研究的主题，包括用于限制学习算法拟合的信息理论和概率策略。