Why might PCA be considered a method of feature selection?

It can handle correlated features
It can improve model performance
It can reduce the dimensionality of the data
It transforms the data into a new space

Principal Component Analysis (PCA) can be considered a method of feature selection because it reduces the dimensionality of the data by transforming the original features into a new set of uncorrelated features. These new features, called principal components, are linear combinations of the original features and are selected to capture the most variance in the data.

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