Final answer:
Rotation is not necessary in PCA, but it can be helpful in certain situations. If you don't rotate the components, the resulting dimensions may still be uncorrelated, but they may not align well with the underlying structure of the data.
Step-by-step explanation:
Rotation is not necessary in PCA, but it can be helpful in certain situations.
PCA is a dimensionality reduction technique that transforms a dataset into a new coordinate system, where the dimensions are ordered by their importance in explaining the variance in the data.
The components obtained from PCA are already orthogonal to each other, meaning they are uncorrelated.
If you don't rotate the components, the resulting dimensions may still be uncorrelated, but they may not align well with the underlying structure of the data.
Rotating the components can help to achieve a simpler and more interpretable representation of the data, by aligning the axes with the underlying patterns