Hello everyone,
I have a query. I could find some papers that randomly divide the data set into train/test (70/30 or 90/10) at different seeds points (number of seeds: 10-15). It means for a given seed, they have a randomized selection of train/test data. The results are then averaged over the different seed points to get a robust estimate of accuracy. Is this an accepted way of doing cross-validation? (though technically it is not a 10-fold cross-validation :))
Thanks!
SR
That sounds good imo. You can do what you like, so long as you explain the method and reasons for using it :)
This approach is OK if all the seed are from the same seed source, same genetically, and have the same seed storage and seed handling history.
Yes Siva. You have to randomize the data before divide the data into training, validation and testing part. Then, average the performance across the fold. This helps to see the performance consistency of the system.
Tats great Dr. Yuvaraj :) Happy to receive a reply from you.
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