Multiple Imputation
Multiple Imputation is a statistical technique used to handle missing data in a dataset. It involves creating multiple plausible imputations for the missing values, which are then analyzed using standard complete-data methods. The imputations are combined to produce estimates and standard errors that reflect the uncertainty due to missing data. Multiple Imputation assumes that the missing data are Missing at Random (MAR), which means that the probability of missingness depends only on observed data and not on unobserved data. This technique is widely used in Data Science and Machine Learning applications where missing data is a common problem.
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