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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.


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


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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