Excludimin

Excludimin is a term used in the field of data analysis and machine learning to describe a technique for handling missing data. It involves excluding observations with missing values from the analysis. This approach is straightforward and can be effective when the proportion of missing data is small and the missing data is not systematic. However, it can lead to a loss of information and potential bias if the missing data is not missing completely at random. Excludimin is often used as a preliminary step in data preprocessing, followed by more sophisticated imputation methods to handle the remaining missing values. The effectiveness of excludimin depends on the nature of the missing data and the specific requirements of the analysis.