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.

翻译：使用最近的机器学习结果 — — 这些结果展示了对不完善和过度装配的信息理论观点 — — 我们证明,即使给予无限的培训时间,确定一个可编码的学习算法是否总是对数据集不合适 — — 也是不可能的。 我们讨论了这一结果的重要性以及进一步研究的潜在议题,包括信息理论和概率策略,以绑定学习算法是否合适。