What are the different imputation methods for missing data?

What are the different imputation methods for missing data?

There are several imputation methods, including mean, median, mode, k-nearest neighbors, and multiple imputation. Would you like to know more about a specific method?

Can you explain how k-nearest neighbors imputation works?

Sure! K-nearest neighbors (KNN) imputation fills in missing values by looking at the 'k' closest data points in the dataset and averaging their values. This method can be effective but may require careful selection of 'k'.

What are the pros and cons of using mean imputation?

Mean imputation is simple and quick, but it can reduce variability and may not be suitable for skewed data. It also assumes that data is missing completely at random.

Are there any advanced methods I should consider?

Yes, advanced methods like multiple imputation and machine learning techniques can provide better estimates by considering the relationships between variables.