"Retrieving original data after PCA"
I use PCA to retain 95% variance. I also get a pre-processing model for ApplyModel but it does not give me original variables responsible for 95% variance. Instead I get a listing of PC-1 etc etc. Help please.
regards
regards
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Thank you for the response. My problem still not solved. Let me give an example:
Suppose I have 4 regular attributes viz. A1,A2,A3,A4 and 1 label viz L. Now after running PCA, it gives me eigen values and vectors which tell that only two principal components PC_1 and PC_2 contribute 99%. For me this is not enough for me. I want to see those two attributes, say, A1 and A3 which are 99% responsible for the variation. Help, required.
regards,
Suppose I have 4 regular attributes viz. A1,A2,A3,A4 and 1 label viz L. Now after running PCA, it gives me eigen values and vectors which tell that only two principal components PC_1 and PC_2 contribute 99%. For me this is not enough for me. I want to see those two attributes, say, A1 and A3 which are 99% responsible for the variation. Help, required.
regards,
I think there is some confusion here. Principal components are linear combinations of the original attributes. Unless the loadings are unit vectors there is no equivalence between a principal component and the original attributes. Rapidminer gives you the components (which are combinations of the original atts). Am I understanding incorrectly your question?
if I get you right you want to see something like an attribute ranking? Maybe then you want to have a look at the Weight by PCA operator. If that is not what you are looking for, please explain a bit more detailed what you are trying to do.
Best regards,
Marius