Tips on Feature Engineering

Tips on Feature Engineering to fit how classifiers work; giving a geometry problem to a tree, oversized dimension to a kNN and interval data to an SVM are not a good ideas remove as much nonlinearities as possible; expecting that some classifier will do Fourier analysis inside is rather naive (even if, it will waste a lot of complexity there) make features generic to all objects so that some sampling in the chain won’t knock them out check previous works – often transformation used for visualisation or testing similar types of data is already tuned to uncover interesting aspects avoid unstable, optimizing transformations like PCA which may lead to overfitting experiment a lot

October 8, 2017 · 1 min · 113 words · C Ried