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If you don't do any feature selection explicitly and use PCA dimensional reduction, you are letting PCA select the features
by j16sdiz 5y ago
If you don't do any feature selection explicitly and use PCA dimensional reduction, you are letting PCA select the features
- hervature 5y agoThis is false though as your top eigenvectors are going to have non-zero entries in all components unless your original data has some features with 0 covariance which is highly unlikely. You still need to collect all the original features, you have not selected features that can be removed.
- LudwigNagasena 5y agoThat’s like saying that in a stacked model every layer is feature selection. That doesn’t really make sense. Feature selection is about which input data is used, not about how it is transformed during the whole prediction procedure.