Feature Labels

[Afbcary]

When I construct the PCA object from a dataset and run getExplainedVariance() I get a number[] of variance values, sorted in descending order. I'd like to know which feature relates to each variance value. I could do this if the report specified the original index of the feature in the sample or allowed me to pass in labels.

I've read the API documentation and pulled down the source code, but I really haven't found much explanation there. Am I missing how to do this? I'd be happy to help improve documentation or add this code if you agree it's missing and necessary.

Use case here (see console)
code

[N8python]

How did you solve this? I'm experiencing a similar problem.

[Afbcary]

@N8python

I think when I wrote this I was fundamentally misunderstanding how PCA works. The variance values relate to new composite features that are combinations of the original features.

I found this article illuminating: https://blog.exploratory.io/an-introduction-to-principal-component-analysis-pca-with-2018-world-soccer-players-data-810d84a14eab

In that article, he ascribes meaning to the newly created components by graphing each data point in the original dimensions as well as the new composite dimensions.