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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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PCA is not recommended for categorical features. There are equivalent algorithms for categorical features like CATPCA and MCA.


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You can apply the Wrapper Sequential Feature Selection (SFS) algorithm. SFS is a family of greedy search algorithms that are used to reduce an initial d-dimensional feature space to a k-dimensional feature subspace where k < d. The idea of the algorithm is to run any classification algorithm (parameter) based on k_features (parameter). It will select ...


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