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 tin- 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?

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B. No No - Principal Components Analysis (PCA) is a dimensionality reduction technique used to transform a large set of variables into a smaller, more manageable set, not a data sampling strategy for addressing class imbalance. PCA does not directly adjust the number of observations per class.

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A. Yes PCA is a dimensionality reduction technique and does not address class imbalance by sampling or modifying class distributions.

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…

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