The reason why this should be done is the scenario when test data set ends up fitting well with new features that is developed based on evaluation of test data set error. One could adopt the 60-20-20% split for training, cross-validation and test data set.
Learning curves could very well help in examining the cases of high bias (under-fitting) or high variance (over-fitting).
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