An intuitive and reliable technique to measure feature importance.
Let's see if I understand this. You say "Simply put, if we randomly shuffle just one feature and everything else stays the same..." Are you saying, you shuffle ONLY one column (feature) in the dataset, correct? This means after the shuffle, the dataset would no longer be valid, since the observations will no longer match the original observations. But I'm thinking this is okay if the "shuffled" observations are only used to determine feature importance, and are not used for modeling. Correct? Please comment.
I agree. You're absolutely right about that according to quantum theory.
Very interesting. How would this work (or would it) with time series data? Thanks!