Exam AWS Certified Machine Learning - Specialty topic 1 question 46 discussion

C looks correct since multiple imputation can be performed based on the related variable as given in the question

Multiple Imputation by Chained Equations or MICE, as per udemy this is always the best answer of all

Why not D: Doesn't Account for Relationships: Mean substitution doesn't take into account the potential relationships between variables. In the scenario you provided, it's believed that other columns could help in reconstructing the missing data. Using only the mean of the missing column doesn't leverage this potential inter-column relationship. Assumption of Missing Completely at Random (MCAR): Mean substitution often operates under the assumption that the data is Missing Completely at Random (MCAR). In reality, data might be missing for a reason, and that reason might relate to other observed variables. Using mean substitution in such cases can introduce biases.

A. NO - Listwise deletion is just dropping rows B. NO - does not reconstruct the data based on other fields C. YES - by definition D. NO - does not reconstruct the data based on other fields

MICE is the algorithm to choose here

Option C

Multiple imputation is a statistical technique for handling missing data that involves generating multiple versions of the dataset with missing values filled in, and then combining the results to produce a single, complete dataset. This approach takes into account the relationship between variables in the dataset, and uses statistical models to predict missing values based on the information in other columns. This helps to preserve the integrity of the dataset by avoiding the introduction of bias or systematic error into the results.

I am trying to understand why Mean Substitution is not the solution. Imputation typically uses the mean if the missing data is random, implying the substitution is not biased.

If it's handling missing data then imputation comes into play Answer is C 100%

https://www.countants.com/blogs/heres-how-you-can-configure-automatic-imputation-of-missing-data/ C

it's C

A common strategy used to impute missing values is to replace missing values with the mean or median value. It is important to understand your data before choosing a strategy for replacing missing values. https://docs.aws.amazon.com/machine-learning/latest/dg/feature-processing.html