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_triszt t1_issbxym wrote

pca

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Sadness24_7 OP t1_issrioz wrote

I dont think PCA will help me, i need to reduce the number of feature in order to simplify the system im working with. those removed feature will no longer be aquired and thus i cant retrain the model in the future. i need to somehow pick 2-10 features out of 38 for which i can finetune the model and deploy it. only those selected features will be logged for future.

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thePedrix t1_isstazu wrote

Maybe you can do the PCA and then check the loadings?

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Sadness24_7 OP t1_isszoev wrote

But what am i looking for tho. i've been looking at loadings matrix for couple minutes but cant really figure out the connections. Lets say i want to select 7 feature out of 38, so i performa pca for 7 components and im looking at loading matrix (correlation between 38 feature's and 7 pca's . do i just look at the component with best correlation with the input features and the 7 highest correlation with that pca component ?

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thePedrix t1_ist0fv6 wrote

I can’t be sure that it would work, but I would try this:

-PCA for N components

-Plot a graph with the 2 or 3 first principal components (depending on the cumulative explained variance, if 2 is enough, a 2D plot)

-Plot the magnitude of the variables and see which are the most impactful. Pick the X features you want.

-Train the network with those X features.

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