You have applied PCA to a dataset and obtained principal components. How would you interpret these components, and what do they represent?
- They represent individual original features
- They represent clusters within the data
- They represent the variance in specific directions
- They represent correlations between features
Principal components represent the directions in the data where the variance is maximized. They are linear combinations of the original features and capture the essential patterns, making it possible to describe the dataset in fewer dimensions without significant loss of information. The other options are incorrect as principal components do not directly represent individual original features, clusters, or correlations.
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