Amirreza Heidari <amirrezaheidary...@gmail.com> writes: > I was reading a tutorial for time series prediction by Neural > Networks. I found that this code have used the same test data in the > following code for validation, and later also for prediction. > > history = model.fit(train_X, train_y, epochs=50, batch_size=72, > validation_data=(test_X, test_y), verbose=2, shuffle=False) > > Does it mean that the validation and test data are the same, or there is a > default percentage to split the data into validation and prediction?
As per Prof. Andrew Ng, training, cross-validation and testing should have three different data-sets. If you have a small example set (for example 10,000 or may be 50,000) then you can split the example set into 60:20:20 ratio for train:validation:testing. But if you have a very large data-set (1 million, 10 million) then consider using 1% or may be lesser for validation and testing. -- Pankaj Jangid -- https://mail.python.org/mailman/listinfo/python-list
