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PCA is really something that one must use whenever it has a multivariables model. It's a very quick check, easy to implement, which avoids basic issues such as:
by wiz21c 3y ago
PCA is really something that one must use whenever it has a multivariables model. It's a very quick check, easy to implement, which avoids basic issues such as: "I thought this variable was important in my model and well, it is not", much like : "don't optimize without measuring first".
- qqqwwweeerrr 3y agoUnless you are strictly talking about predictive modeling, I would disagree with this. PCA just tries to represent N-dimensional observations in a k < N dimensional subspace (for a given k) such that it captures the most variation. This does not mean that any obtained component loadings refer to anything real or meaningful.
- wrp 3y agoTo clarify your point, we need to distinguish between pruning dimensions and projecting onto a k < N set of new orthogonal dimensions. The name of PCA does make it sound like you are selecting dimensions.
- jackthetab 3y ago> The name of PCA does make it sound like you are selecting dimensions. I certainly thought that! Using somes terms from the previous comment, does this mean that the k < N subspace is not (necessarily) a subset of the N-space? Or is the subspace a subset of the data with a different coordinate system? (Yes, I'm still trying to intuitively grasp these ideas.)