I am doing RFE with 18 features. My understanding of RFE is, it starts by trying N - 1 models, and then removing the feature that causes the N - 1 models to perform worst. Then it tries N - 2 models, and so on. I've also read elsewhere that RFE does some sort of weighting. Which one is it? Weighting to me indicates a more math-y approach that requires more interpretation of the "optimal" features, whereas doing the N - 1, N - 2 indicates to me more of a brute force approach that requires less interpretation of the "optimal" features.
My model specifically said the highest accuracy was with 18 features. However, the suspect aspect was how the accuracy stayed the same when RFE was run with 0 through 15 features (see picture).
Does this indicate that something might be wrong with my RFE model? If so, what could be wrong?