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Questions and Answers
What is the main purpose of dimensionality reduction?
What is the main purpose of dimensionality reduction?
- To avoid the curse of dimensionality (correct)
- To increase the number of irrelevant features
- To complicate data mining
- To visualize data in higher dimensions
What does Principal Component Analysis (PCA) do?
What does Principal Component Analysis (PCA) do?
- Adds noise to the data
- Increases the dimensionality of data
- Transforms high-dimensional data into lower dimensions while retaining information (correct)
- Has no impact on data dimensionality
What does PCA aim to capture in data?
What does PCA aim to capture in data?
- Irrelevant features
- The largest amount of variation (correct)
- The smallest amount of variation
- Noise
What happens to the original data in PCA?
What happens to the original data in PCA?
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What does dimensionality reduction allow in data mining?
What does dimensionality reduction allow in data mining?
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