You have a dataset with hundreds of features, some of which are redundant. How would you approach reducing the dimensionality?
- Remove all redundant features manually
- Apply PCA
- Use only the first few features
- Normalize the data
Applying Principal Component Analysis (PCA) would be the most efficient way to reduce dimensionality in this scenario. PCA transforms the data into a new set of uncorrelated features, effectively capturing the most important variance in fewer dimensions, and thus removing redundancy. Manually removing redundant features may not be practical with hundreds of features, and other options do not directly address dimensionality reduction.
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