title: Nested cross-validation really necessary?
body: <p>I have a problem with my cross-validation procedure. Currently I'm just looping (iterating) over my paramaters (e.g. C and gamma for RBF SVM) and doing an inner Leave-one-out cross-validation.</p>
  
  <p>Of course this can introduce bias. So it would be better to do a nested cross-validation, for example an outer leave-one-out cross-validation and an inner 10-fold cross-validation.</p>
  
  <p>The problems are that I only have 37 data points, a nested cross-validation increases the runtime by a lot and I also want to print for example for k nearest neighbours k against the accuracy or the number of hidden layers agains the accuracy (for neural network) which is not possible in nested cross-validation.</p>
  
  <p>Is there really a big bias without using nested cross-validation or can I go with my single CV approach? Are there some papers which claim that a single cross-validation is ok?</p>