What does the Metadata Advisor do with variables that have more than 50% missing values?

What does the Metadata Advisor do with variables that have more than 50% missing values?

• A. Includes them in the analysis
• B. Rejects them
• C. Flags them for review
• D. Models them with average values

Answer: (B)

The Metadata Advisor is designed to assess the quality of the variables being considered for analysis. When a variable is identified as having more than 50% missing values, it is deemed unreliable for inclusion in predictive modeling or analysis. This high level of missing data suggests that the variable may not provide meaningful information and could potentially bias the results. Therefore, the advisor opts to reject such variables outright to maintain the integrity and robustness of the analysis. Rejecting these variables helps focus on more reliable and complete data that can yield more accurate and actionable insights in the modeling process.

The Metadata Advisor is designed to assess the quality of the variables being considered for analysis. When a variable is identified as having more than 50% missing values, it is deemed unreliable for inclusion in predictive modeling or analysis. This high level of missing data suggests that the variable may not provide meaningful information and could potentially bias the results. Therefore, the advisor opts to reject such variables outright to maintain the integrity and robustness of the analysis. Rejecting these variables helps focus on more reliable and complete data that can yield more accurate and actionable insights in the modeling process.