Re:Principal Component Analysis Explained Visually
by oivey on Monday May 03, 2021 @12:59AM
(#27021753)
When/if used properly, no. The idea behind PCA is to find a set of features with far less dimensionality than the original data. The hope/intent with this sort of approach is that any more fitted features are just fitting noise.
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Re:Principal Component Analysis Explained Visually
by hervature on Monday May 03, 2021 @01:38AM
(#27021954)
For people who are curious, the GP is correct when it comes to fitting the training data. Recall, with enough parameters, we can get 100% on training. The parent’s comment is about testing/validation where we want to avoid overfitting so removing the least important parameters can be helpful.