my goal is to run a wrapper-based feature selection on ~70 nominal features to select a the ~10 best ones. I think a forward selection is the best choice here as it starts with no features and adds one new feature at a time. I read through several guides here on how to do a wrapper-based feature selection that were very helpful in implementing this.
However I am still lost on which classifiers I should select inside the model. I will not use the resulting dataset to train and test a model afterwards, so the obvious choice of selecting the same classifier as I would for the model is not there. Are there any posts here I missed so far that would help me with selecting classifiers? Or can you share your knowledge and experience on this with me? I greatly appreciate your answers!