Regression imputation is a statistical technique used to estimate and replace missing values in a dataset by predicting them based on the relationships found in other observed data points. This method leverages regression analysis, where the values of missing data are predicted using regression equations derived from existing data. It effectively combines data cleaning and handling missing data by filling gaps while preserving the underlying structure of the dataset, which is also critical for feature selection and engineering.
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