query: Can dev (development) set be part of a training set in machine learning?
positive_passages.docid: 155648
positive_passages.text: Should final (production ready) model be trained on complete data or just on training set?
negative_passages.docid: 88274
negative_passages.text: Is it possible for test error to be lower than training error
negative_passages.docid: 93969
negative_passages.text: Is it possible to learn a neural networks for a constant target?
negative_passages.docid: 104621
negative_passages.text: On the hardness of data to learn
negative_passages.docid: 84469
negative_passages.text: Is there a thumb-rule for designing neural-networks?
negative_passages.docid: 82881
negative_passages.text: How to do data augmentation and train-validate split?
negative_passages.docid: 92956
negative_passages.text: Is it possible to train a neural network without backpropagation?
negative_passages.docid: 16099
negative_passages.text: Understanding /dev and its subdirs and files
negative_passages.docid: 648
negative_passages.text: How should Feature Selection and Hyperparameter optimization be ordered in the machine learning pipeline?
negative_passages.docid: 69925
negative_passages.text: Can Predict/Classify be parallelized?
negative_passages.docid: 17831
negative_passages.text: Training on the full dataset after cross-validation?
negative_passages.docid: 77815
negative_passages.text: Machine Learning with intended missing values
negative_passages.docid: 21982
negative_passages.text: How to know the internal pre-processing of automatic machine learning function `Classify`?
negative_passages.docid: 73962
negative_passages.text: Is it always better to use the whole dataset to train the final model?
negative_passages.docid: 153285
negative_passages.text: Is hyperparameter tuning on sample of dataset a bad idea?
negative_passages.docid: 44572
negative_passages.text: List of feature engineering techniques
negative_passages.docid: 149016
negative_passages.text: Is it feasible to compile Python to machine code?