Data Preprocessing Questions
Data imputation using expectation-maximization (EM) is a statistical technique used in data preprocessing to fill in missing values in a dataset. It is based on the assumption that the missing data is missing at random (MAR). The EM algorithm iteratively estimates the missing values by maximizing the likelihood function, taking into account the observed data and the current estimates of the missing values. This process continues until convergence is achieved, resulting in imputed values for the missing data. EM imputation is particularly useful when dealing with datasets with missing values, as it allows for the inclusion of incomplete data in subsequent analyses.