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Quick Check 23.4 – Data 100, Summer 2020
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* Indicates required question
PCA is appropriate for EDA when:
*
The data we're exploring are already low-dimensional
The data we're exploring are inherently low rank
We wish to identify clusters of similar observations in a high-dimensional dataset
None of the Above
Required
What do the columns of V (or the rows of V^T) tell us?
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The weights of the data points corresponding to our principal components
The weights of the features corresponding to our principal components
The amount of variance our principal components capture
The fraction of variance our principal components capture
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