Question
Download Solution PDFDimension reduction methods have the goal of using the correlation structure among the predictor variables to accomplish which of the following:
A. To reduce the number of predictor components
B. To help ensure that these components are dependent
C. To provide a framework for interpretability of the results
D. To help ensure that these components are independent
E. To increase the number of predictor components
Choose the correct answer from the options given below:
Answer (Detailed Solution Below)
Detailed Solution
Download Solution PDFThe correct answer is A, C and D only,
Key Points The correct answer is Options A, C, and D only.
- Option A: Dimension reduction methods are used to reduce the number of predictor components. This is done by identifying the underlying patterns in the data and then representing the data in a lower-dimensional space.
- Option C: Dimension reduction methods can provide a framework for interpretability of the results. This is because it can help to simplify the data and make it easier to understand the relationships between the variables.
- Option D: Dimension reduction methods can help to ensure that these components are independent. This is because the goal of dimension reduction is to identify the underlying patterns in the data, and independent components do not share any common patterns.
- Option B is incorrect because dimension reduction methods do not necessarily ensure that the components are dependent. In fact, the goal of dimension reduction is to identify the underlying patterns in the data, and independent components do not share any common patterns.
- Option E is incorrect because dimension reduction methods are used to reduce the number of predictor components, not increase them
Last updated on Jun 6, 2025
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