Bottom line up front: is there any reason not to center and scale continuous variables prior to model fitting for the sake of conducting model comparison?
I'm conducting a model comparison on a large data set (80,000 instances x 300 attributes) and I'm looking at predicting 250 different response values. If I compare 5 inducers- let's say cubist, boosted trees, random forest, MARS, and kNN- I'm already looking at 1,250 model fits without doing any parameter tuning (and counting the ensemble methods as a single fit). Although this is just the exploratory phase, I know that some models are sensitive to center and scaling (like kNN) and others aren't. Am I conducting my due diligence in comparing these models on an even playing field if I center and scale all of my numeric features so that I can use one predictor matrix for all 250 response vectors rather than mixing and matching? Or can some algorithms actually suffer from variables being transformed?
Note that I am not at all worried about interpretability of the resulting model.