The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent and identically distributed (iid), in which case trained models are vulnerable to out-of-distribution generalization problems. Of particular interest are problems where data correspond to observations made on phylogenetically related samples (e.g. antibiotic resistance data).

中文翻译：

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系统发育分布数据的监督学习

开发健壮的机器学习（ML）模型的能力被认为对于在生物学和医学领域采用ML技术至关重要。当可用于训练的数据不是独立且分布不均（iid）时，这一挑战尤为严峻，在这种情况下，训练后的模型容易受到分布外泛化问题的影响。特别令人感兴趣的是数据与系统发育相关样品的观察值相对应的问题（例如抗生素耐药性数据）。