Databricks Certified Machine Learning - Associate
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In a dataset with a numerical feature 'Salary', you have noticed that some values are missing. You have decided to use a regression model to predict the missing values based on other features in the dataset. Explain the process of using a regression model for imputation and discuss the potential benefits and limitations of this approach.
A
Using a regression model for imputation is not possible, as regression models require complete data to make predictions.
B
Using a regression model for imputation involves training the model on the observed values of 'Salary' and using it to predict the missing values. This approach can capture the relationships between 'Salary' and other features, but may introduce bias if the model is not well-calibrated.
C
Using a regression model for imputation is the best approach, as it can handle any type of missing data mechanism and provide accurate predictions.
D
Using a regression model for imputation is only useful for linear relationships between 'Salary' and other features, and may not work well for non-linear relationships.