The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
Do you need (or can you) use split or cross- validation if you're using Deep Learning?
Would it be correct to use deep learning in training partition?
Tagged:
0
Best Answers
-
varunm1 Member Posts: 1,207 UnicornHi @Curious
You can use cross-validation for deep learning as the results are consistent compared to split validation. Personally, I use cross-validation for every model I develop as this gives confidence about the results.
Thanks
Regards,
Varun
https://www.varunmandalapu.com/Be Safe. Follow precautions and Maintain Social Distancing
7 -
SGolbert RapidMiner Certified Analyst, Member Posts: 344 UnicornHi @Curiousit is correct, you can take a look at Auto Model with model type "Deep Learning" to have an example. It is always better to do cross validation, and maybe reduce the folds if it takes too long.It is good to remark that cross validation is used only for measuring the model performance with a reasonable bias. Depending of whether you need to optimize hyperparameters or not, cross validation may not be needed in a production environment.Regards,Sebastian
6