What are the best practices in data imputation?

What are the best practices in data imputation?

Best practices in data imputation include understanding the data distribution, using appropriate methods like mean, median, or KNN, and validating the imputed data.

Can you explain why understanding data distribution is important?

Understanding data distribution helps in choosing the right imputation method, ensuring that the imputed values are realistic and maintain the integrity of the dataset.

What methods are commonly used for imputation?

Common methods include mean/mode/median imputation, K-Nearest Neighbors (KNN), regression imputation, and multiple imputation.

How do I validate the imputed data?

You can validate imputed data by comparing it with original data distributions, checking for consistency, and using cross-validation techniques.