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amoeba
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Assessing principal What are good metrics to assess the quality of a PCA fit, in order to select the number of components analysis?

What is a good metric for assessing the quality of a pcaprincipal component analysis (PCA)? 

I performed this algorithm on a dataset. My objective was to reduce the number of features (the information was very redundant). I know the percentage of variance kept is a good indicator of how much information we keep, be are there other information metrics I can use to make sure I removed redundant information and didn't 'lose' such information?

Assessing principal components analysis

What is a good metric for assessing the quality of a pca? I performed this algorithm on a dataset. My objective was to reduce the number of features (the information was very redundant). I know the percentage of variance kept is a good indicator of how much information we keep, be are there other information metrics I can use to make sure I removed redundant information and didn't 'lose' such information?

What are good metrics to assess the quality of a PCA fit, in order to select the number of components?

What is a good metric for assessing the quality of principal component analysis (PCA)? 

I performed this algorithm on a dataset. My objective was to reduce the number of features (the information was very redundant). I know the percentage of variance kept is a good indicator of how much information we keep, be are there other information metrics I can use to make sure I removed redundant information and didn't 'lose' such information?

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bigTree
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Assessing principal components analysis

What is a good metric for assessing the quality of a pca? I performed this algorithm on a dataset. My objective was to reduce the number of features (the information was very redundant). I know the percentage of variance kept is a good indicator of how much information we keep, be are there other information metrics I can use to make sure I removed redundant information and didn't 'lose' such information?