Designing and Implementing a Data Science Solution on Azure — Question 33

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Principal Components Analysis (PCA) sampling mode.
Does the solution meet the goal?

Answer options

Correct answer: B

Explanation

The proposed solution does not meet the goal because Principal Components Analysis (PCA) is primarily a dimensionality reduction technique and does not specifically address class imbalance through sampling. Instead, methods like oversampling the minority class or undersampling the majority class would be more appropriate strategies for handling class imbalance.