LFD Book Forum  

Go Back   LFD Book Forum > Book Feedback - Learning From Data > Chapter 5 - Three Learning Principles

Thread Tools Display Modes
Prev Previous Post   Next Post Next
Old 07-09-2021, 05:33 PM
gverhoev gverhoev is offline
Junior Member
Join Date: Jul 2021
Posts: 6
Default Is a test set needed after cross validation?

It is said that the CV error Ecv is an unbiased estimate of Eout (N-1), hence it is used for model selection.
However, many books say that after CV, one should have a third dataset (often called the test dataset) to truly measure the performance (i.e. Eout) of the chosen final hypothesis. However, if Ecv is already an unbiased estimate of Eout (N - 1), why would one then even need this third / test set to check how well the model does? Is there something that I miss here?
Is this maybe because the CV approach is technically data snooping (because we make a model choice influenced by the data, so the data has less ability to evaluate the final outcome), so it is still best to test the real performance on never seen data? Or are these books simply wrong?
Reply With Quote

cross validation, data snooping, out-of-sample error, test set

Thread Tools
Display Modes

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

Forum Jump

All times are GMT -7. The time now is 04:21 PM.

Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2022, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.