I see lots of papers that use just train and test datasets, without a third validation set, but they use cross-validation so that every data point is used for training and testing among the different folds of cross-validation. If you do X-fold cross-validation and find that the test accuracy is about the same across folds, is this better justified/more robust than having one training set, one test set, and a third validation (a la Kaggle)?
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