Using Multiple Imputation by Chained Equations (MICE) for Missing at Random Data

• ✓ A.The mechanism of missingness (MCAR, MAR, MNAR).
• B.The class imbalance of the target variable.
• ✓ C.The percentage of missing values in each feature.
• ✓ D.The distribution of the feature (e.g., skewed, normal).
• E.The feature importance according to a random forest model.

Why A: The three correct considerations are: missing data mechanism (MCAR/MAR/MNAR) which determines whether imputation can be unbiased; percentage of missing values in each feature, which affects the reliability of imputation and whether deletion is preferable; and feature distribution (e.g., skewed, normal), which guides the choice between mean, median, or model-based imputation. Option B (class imbalance) is a consideration for classification models, not imputation. Option E (feature importance) is not a standard criterion for choosing imputation methods.

• A.Last observation carried forward
• ✓ B.Multiple imputation using MICE
• C.Listwise deletion
• D.Mean imputation

Why B: Option B is correct because Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because last observation carried forward is only appropriate for time series data where missing values are filled with the previous observation; it does not handle non-random missing data correlated with other features. Option C is wrong because listwise deletion reduces sample size and may introduce bias when data is not missing completely at random. Option D is wrong because mean imputation can bias estimates and reduce variability, especially when missingness is related to other features.

• ✓ A.Mean imputation
• B.Model-based imputation using linear regression
• C.k-Nearest Neighbors imputation
• D.Multiple imputation using chained equations

Why A: Mean imputation (Option A) should be avoided when data is not missing completely at random (NMAR) because it can introduce bias by underestimating variance and distorting the relationships between variables. Options B (model-based imputation), C (k-NN imputation), and D (multiple imputation) are more robust for non-random missing data as they account for patterns in the data and produce less biased estimates.

• A.The imputation will introduce bias if the missing values are not random.
• B.Imputation using median is computationally expensive for large datasets.
• ✓ C.The imputed values may reduce the variance of the 'age' distribution.
• D.The imputed values will increase the variance of the feature, leading to overfitting.

Why C: Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.