I really enjoyed learning about the techniques used in machine learning to select an ideal data set when there is no target model (compared to the paper, where

I really enjoyed learning about the techniques used in machine learning to select an ideal data set when there is no target model (compared to the paper, where the model is known). A common practice is called k-fold cross-validation, where you train with k-1 subsets of D, then do validation with the kth set. You rotate through k times, so that every subset serves as the validation set once. The results can then be combined to produce a model which is usually much more effective than just setting aside part of D for training and part for validation. Like machine teaching, where the goal is to minimize D, this is based on the assumption that some data points are much better for training the model than others. http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29#k-fold_cross-validation http://en.wikipedia.org/wiki/Cross-validation_%28statistics%...