query: Do you merge your validation and training sets?
positive_passages.docid: 17831
positive_passages.text: Training on the full dataset after cross-validation?
negative_passages.docid: 9079
negative_passages.text: Mark-as-duplicate vs. Merge
negative_passages.docid: 134716
negative_passages.text: Simultaneous fit to three datasets with measurement errors using different models but same parameters
negative_passages.docid: 8703
negative_passages.text: How to apply standardization/normalization to train- and testset if prediction is the goal?
negative_passages.docid: 18820
negative_passages.text: For imbalanced classification, should the validation dataset be balanced?
negative_passages.docid: 64707
negative_passages.text: Use of nested cross-validation
negative_passages.docid: 156460
negative_passages.text: Expanding a dataset based on a weight field
negative_passages.docid: 97817
negative_passages.text: How to switch from List of rules to Association?
negative_passages.docid: 14625
negative_passages.text: Using AutoMerge Editing - things aren't merging, what isn't right?
negative_passages.docid: 77851
negative_passages.text: Should we apply normalization to test data as well?
negative_passages.docid: 19145
negative_passages.text: Validation of machine learning algorithms implementation
negative_passages.docid: 83655
negative_passages.text: Assumptions behind cross-validation
negative_passages.docid: 112972
negative_passages.text: Are there any tools for merging CSS?
negative_passages.docid: 84407
negative_passages.text: How to judge if a supervised machine learning model is overfitting or not?
negative_passages.docid: 91800
negative_passages.text: Basic question about Merging faces
negative_passages.docid: 9064
negative_passages.text: Is it a good practice to show a sign-up or registration form as seperate parts rather than in one go?
negative_passages.docid: 9663
negative_passages.text: Is it a good practice to always scale/normalize data for machine learning?