How do validation check work in Neuralnet ?
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I'm learning about the neural network in MATLAB. when I learn about the neural net, I don't see anything about validation check (usually data is divided by 2 training and test testing) but in Matlab, they have a part for validation and have Validation check(in figure = 6).
so what I want to know is why we need validation check and how it work to check
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Accepted Answer
Greg Heath
on 7 Aug 2017
Edited: Greg Heath
on 11 Jul 2018
design = train + validate
train : Weight Estimation
validate: Not directly involved in weight estimation. Protects ability to generalize to nontraining data. Stops training when the nontraining val subset error rate increases CONTINUOUSLY for more than 6 (default) epochs.
val subset error rate is therefore SLIGHTLY biased.
test subset error rate is COMPLETELY unbiased
default division ratio = 0.7/0.15/0.15
If val stopping occurs, take a look at the error rate curves and you will see why training was stopped.
OBVIOUSLY, the most unbiased approach for constant timestep timeseries prediction is to use DIVIDEBLOCK data division with the validation subset in the middle.
Hope this helps
Thank you for formally accepting my answer
Greg
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Greg Heath
on 11 Jul 2018
Edited: Greg Heath
on 11 Jul 2018
Thanks for the heads up! Changed to:
OBVIOUSLY, the most UNBIASED approach for constant timestep timeseries prediction is to use DIVIDEBLOCK data division with the validation subset in the middle.
GREG
Moritz Hesse
on 2 May 2019
There is also a Mathworks article on this here: https://uk.mathworks.com/help/deeplearning/ug/train-and-apply-multilayer-neural-networks.html#bss331l-17
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