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Data imputation is the most popular method of dealing with missing values,but in most real life applications, large missing data can occur and it isdifficult or impossible to evaluate whether data has been imputed accurately(lack of ground truth). This paper addresses these issues by proposing aneffective and simple principal component based method for determining whetherindividual data features can be accurately imputed - feature imputability. Inparticular, we establish a strong linear relationship between principalcomponent loadings and feature imputability, even in the presence of extrememissingness and lack of ground truth. This work will have importantimplications in practical data imputation strategies.

Predicting feature imputability in the absence of ground truth