How does Principal Component Analysis (PCA) work as a method of dimensionality reduction?
- By classifying features
- By maximizing variance
- By minimizing variance
- By selecting principal features
Principal Component Analysis (PCA) works by transforming the original features into a new set of uncorrelated features called principal components. It does so by maximizing the variance along these new axes, meaning that the first principal component explains the most variance, the second explains the second most, and so on.
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