  Classifying samples in incomplete datasets is a common aim for machine
learning practitioners, but is non-trivial. Missing data is found in most
real-world datasets and these missing values are typically imputed using
established methods, followed by classification of the now complete, imputed,
samples. The focus of the machine learning researcher is then to optimise the
downstream classification performance. In this study, we highlight that it is
imperative to consider the quality of the imputation. We demonstrate how the
commonly used measures for assessing quality are flawed and propose a new class
of discrepancy scores which focus on how well the method recreates the overall
distribution of the data. To conclude, we highlight the compromised
interpretability of classifier models trained using poorly imputed data.
