Regression Imputation
Regression imputation is a data imputation technique used in data science to fill in missing values in a dataset. It involves using regression analysis to predict the missing values based on the relationship between the missing variable and other variables in the dataset. The regression model is trained on the observed values of the variable with missing data and other variables that are correlated with it. Once the model is trained, it is used to predict the missing values. Regression imputation is a popular imputation technique because it can handle missing data in both continuous and categorical variables. However, it assumes that the relationship between the missing variable and other variables in the dataset is linear and may not work well if the relationship is non-linear or complex.
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