Worse results when training on entire dataset

After finalizing the architecture of my model I decided to train the model on the entire dataset by setting validation_split = 0 in fit(). I thought this would improve the results based on these sources:

What is validation data used for in a Keras Sequential model?

Your model doesn't "see" your validation set and isn´t in any way trained on it  

https://machinelearningmastery.com/train-final-machine-learning-model/

What about the cross-validation models or the train-test datasets?    They've been discarded. They are no longer needed.  They have served their purpose to help you choose a procedure to finalize.  

However, I got worse results without the validation set (compared to validation_split = 0.2), leaving all other parameters the same.

Is there an explanation for this? Or was it just by chance that my model happened to perform better on the fixed test data when a part of the training data was excluded (and used as validation).

https://stackoverflow.com/questions/66046148/worse-results-when-training-on-entire-dataset February 04, 2021 at 08:59PM