A dataset for ML training contains 500 features, many of which are correlated. Training is slow and model performance is poor due to the curse of dimensionality. Which technique reduces the number of features while retaining maximum variance?

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C. Principal Component Analysis (PCA). PCA transforms the original correlated features into a smaller set of orthogonal principal components that capture the maximum variance in the data, reducing dimensionality while retaining most of the information.

A dataset for ML training contains 500 features, many of which are correlated. Training is slow and model performance is poor due to the curse of dimensionality. Which technique reduces the number of features while retaining maximum variance?

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