## What is Expectation-Maximization?

Use the iterative EM algorithm to impute missing observations using the “most likely” values based on the complete observations.

Machine Learning Interview Questions

Use the iterative EM algorithm to impute missing observations using the “most likely” values based on the complete observations.

Replace the missing values with the average of the observation’s k-nearest neighbors.

Replace the missing values with an arbitrary value located at the far end of the distribution of the feature, for example 999

Replace the missing values with the most frequently occurring value from the non-missing observations.

Mean Imputation involves replacing the missing values with the mean of the non-missing observations.

Mean Imputation, Mode Imputation, Extreme Value Imputation, Nearest Neighbor Imputation, Expectation Maximization

Different categories of missing data are: (a) Missing Completely at Random (MCAR), (b) Missing at Random (MAR), and (c) Missing Not at Random