How to interpret PCA rankings in Weka

I am struggling to understand what the rankings in Weka are representing. I.e. the coefficients for each attribute in the rank. What is the output in the Weka program for PCA telling me with these rankings? And how does this help me feature select attributes? Because right now its making no sense how they are ranked.

My data set is 31000 rows with 13 attributes. My class is Income_brack > or = 50k

Topic pca weka feature-selection machine-learning

Category Data Science

1
answered at

The ranking is derived form the size of the eigenvalue of the principal component (largest on top) and the scores represent 1 - cumulative sum of the variance.

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