  In a world where Machine Learning (ML) is increasingly deployed to support
decision-making in critical domains, providing decision-makers with
explainable, stable, and relevant inputs becomes fundamental. Understanding how
machine learning works under missing data and how this affects feature
variability is paramount. This is even more relevant as machine learning
approaches focus on standardising decision-making approaches that rely on an
idealised set of features. However, decision-making in human activities often
relies on incomplete data, even in critical domains. This paper addresses this
gap by conducting a set of experiments using traditional machine learning
methods that look for optimal decisions in comparison to a recently deployed
machine learning method focused on a classification that is more descriptive
and mimics human decision making, allowing for the natural integration of
explainability. We found that the ML descriptive approach maintains higher
classification accuracy while ensuring the stability of feature selection as
data incompleteness increases. This suggests that descriptive classification
methods can be helpful in uncertain decision-making scenarios.
