How or when to use cross-validation (model selection, parameter tuning,..)

by Ben   Last Updated October 09, 2019 08:19 AM - source

I know there are many questions about cross validation but I couldn't find that generally describes all aspects (I have in my mind). In How to evaluate the final model after k-fold cross-validation it is described that cross validation is used to select the best model. I wonder about the following:

  1. Why are different models not just trained on the same data set and tested on the same separated dataset? Why the effort for using a different data set for each model?
  2. The same applies when searching the best parameters for a single model (like in grid search).

Can you please clear my questions?


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