I have been reading the book Hands of ML of Geron and a question arose for me the PCA you do after Normalization.

  1. let's say u do a cross validation u do multiple PCA's, there could be a bug in some fold, e.g in a training set a feature will be removed from PCA and to other folds this feature will remain? (for modeling pca, not for scatter plot)

2)and what u do with categorical data and numeric ( I do understand is for continius variables and not for discrete)

thank you in advance

  • 1
    $\begingroup$ When you perform PCA, none of the features will be removed. They will be converted to principal components which are linear combinations of the features. Let’s say you have 10 features and do pca, you get 10 principal components. So technically none of the feature is removed. Let me know if I am missing something here please. $\endgroup$ Feb 8 at 3:26


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