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while training and test sets would come from a cohort of patients, the "validation", such as discovery of the same variants, would be done with an entire different cohort, coming from a different study. For 20 years, I used training, test, and validation datasets that way. I was utterly baffled when I discovered that the modern deep learning community decided otherwise. And the confusion is still here. See the illustrations of
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I can't seem to find here or in other places the earliest source for this method. it seems the holdout method was separately proposed by
Highleyman in 1962, and cross validation was separately proposed by Stone in 1974, but the mixture of those two method resulting the train/validation/test is yet to
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In several areas of science, e.g. in bioinformatics, the test set is used during the development of a software or the training of a model. The validation is done on a completely different dataset, similar to the validation of an hypothesis or a theory elsewhere ins cience. For instance, in genomics,
462:, it says that in statistics and machine learning, gold standard is "a manually annotated training set or test set". What does it mean that the test set is manually annotated? And is "gold standard" a term that is important enough to be mentioned in this article perhaps? —
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with references to information science, statistics, data mining, biostatistics, etc. Currently the two articles are near duplicates (or could be based on the available information. Can we imagine some information for either which is not relevant for the other?
618:- split into training/validation/test sets? This article is written in the context of Machine Learning, and often when training/validation/test sets are sampled from the main data source they are done so either randomly or in a
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Totally agree with the suggestion - training set, testing set and validation set are all parts of one whole and should be presented in one topic. (MM-Professor of QM & MIS, WWU-USA)
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be credited to one person. is this the truth? earliest source here is the Bishop book in 1995, but I don't think he is the one responsible for proposing this in literature
439:.) Perhaps a link should be created so that looking up "discovery set" redirects to here. Now, "discovery set" just gets a bunch of mostly-irrelevant search results.
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It's not clear in _whose_ practice this terms are flipped. In lots of posts by recognized practitioners (e.g. ) they're not flipped.
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I have seen the term "gold standard" been used at a few places in connection with articles about machine learning. On the page
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A training set is also called a discovery set, right? (See for example <DOI: 10.1056/NEJMoa1406498: -->
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for that content in the latter page, and it must not be deleted as long as the latter page exists.
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on
Knowledge. If you would like to participate, please visit the project page, where you can join
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way. I think that this is worthy of mention in this article, even if not in detail.
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I agree they should be merged. Both articles say as much in their introductions.
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The traditional meaning of validation is described at
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