During an exploratory data analysis on a dataset intended for a predictive modeling task, you find that a categorical feature, referred to as feature A, shows significant predictive capability. However, there are instances where values for feature A are missing. What approach should you take to handle the missing values in feature A without losing its predictive power?

Exam-Like

Drop feature A if more than 15% of values are missing. Otherwise, use feature A as-is.

6.0%

Compute the mode of feature A and then use it to replace the missing values in feature A.

13.3%

Replace the missing values with the values of the feature with the highest Pearson correlation with feature A.

14.5%

Add an additional class to categorical feature A for missing values. Create a new binary feature that indicates whether feature A is missing.

66.3%

Google Professional Machine Learning Engineer