Comments (1)
Why is there a need for a training, validation, and a test dataset? Wouldn't training & test suffice?
The goal is to let you use the validation data to tune hyperparameters such as model architecture, etc. You shouldn't use the test dataset for this.
Isn't the 20% of training data we're throwing away valuable to improve the model's performance?
Yes, it is. In the real world, once you've achieved a good architecture, you would retrain it on all available data (minus the test dataset).
from keras-io.
Related Issues (20)
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from keras-io.