Talk:Training, validation, and test data sets

This is an old revision of this page, as edited by Prax54 (talk | contribs) at 15:55, 20 June 2015 (Merge). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Latest comment: 10 years ago by Prax54 in topic Merge
WikiProject iconStatistics Start‑class Mid‑importance
WikiProject iconThis article is within the scope of WikiProject Statistics, a collaborative effort to improve the coverage of statistics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.
StartThis article has been rated as Start-class on Wikipedia's content assessment scale.
MidThis article has been rated as Mid-importance on the importance scale.
WikiProject iconRobotics Start‑class Low‑importance
WikiProject iconThis article is within the scope of WikiProject Robotics, a collaborative effort to improve the coverage of Robotics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.
StartThis article has been rated as Start-class on Wikipedia's content assessment scale.
LowThis article has been rated as Low-importance on the project's importance scale.

Merge

There is absolutely no value added of having two articles Training set and Test set separately when neither can be discussed alone. The concept is Training and test sets 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? Sda030 (talk) 22:53, 27 February 2014 (UTC)Reply

I agree they should be merged. Both articles say as much in their introductions. Prax54 (talk) 04:03, 10 January 2015 (UTC)Reply
Merger done, some rewrites needed.

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)