I recently discovered the RFE tool, and love it. I'd like to understand how this is different from vanilla backward elimination.

Despite lots of information about these two techniques, the penny doesn't seem to drop for me.

Here, the answer intimates that they are essentially the same thing.

Here, the writer suggests that RFE targets individual variable coefficients (I assume p-values or maybe effect size?), whereas Backward Elimination tries to achieve the lowest AIC score for the model as a whole.

Here, the writer suggests that RFE is a type of Backward Elimination, although the explanation is hard to decipher, and the essential difference is not addressed.

So, it RFE just Backward Elimination done by a data scientist, not a statistician?

I recently discovered the RFE tool, and love it. I'd like to understand how this is different from vanilla backward elimination.

Despite lots of information about these two techniques, the penny doesn't seem to drop for me.

Here, the answer intimates that they are essentially the same thing.

Here, the writer suggests that RFE targets individual variable coefficients (I assume p-values or maybe effect size?), whereas Backward Elimination tries to achieve the lowest AIC score for the model as a whole.

Here, the writer suggests that RFE is a type of Backward Elimination, although the explanation is hard to decipher, and the essential difference is not addressed.

So, is RFE just Backward Elimination done by a data scientist, not a statistician?