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DP-100 · Question #120

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…

The correct answer is B. No. PCA (Principal Component Analysis) is a dimensionality reduction technique that creates new orthogonal components from linear combinations of existing features. It does not address class imbalance - it has no mechanism to increase representation of underrepresented classes…

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Question

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?

Options

  • AYes
  • BNo

How the community answered

(51 responses)
  • A
    20% (10)
  • B
    80% (41)

Explanation

PCA (Principal Component Analysis) is a dimensionality reduction technique that creates new orthogonal components from linear combinations of existing features. It does not address class imbalance - it has no mechanism to increase representation of underrepresented classes. Applying PCA when one class has very few observations would still leave those observations in the minority; the components would still reflect the dominant class's variance. To handle class imbalance, sampling strategies such as oversampling (SMOTE) or undersampling the majority class are required. The solution does not meet the goal.

Topics

#Class Imbalance#Data Sampling#Dimensionality Reduction#PCA

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