Exam Certified Machine Learning Associate topic 1 question 5 discussion

correct answer is D

the answer is D Creating a binary feature variable (also known as a missing indicator) for each feature that contained missing values is a common technique to retain information about the missingness itself. This approach allows the model to potentially learn patterns related to the missingness of data, which can be informative. Benefits of Creating Binary Indicators Retains Information: By adding a binary indicator, you preserve the information about which values were originally missing. This can be useful if the fact that a value is missing carries predictive power. Improves Model Performance: In some cases, the pattern of missing data can be correlated with the target variable. Including this information can help improve the model's performance. Flexibility: This method allows you to impute missing values (e.g., with the median) while still providing the model with additional context about the data.