Should I train my final model on the (train+validation) set before final submission?

I was trying to evaluate different classification models on MNIST dataset.

There are two datasets provided : \`train\` - 42000 images, and \`test\` - 28000 images.

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I first divided the original training dataset (42000 images) into a (80:20 split ) of \`train\_set\` (33600) and \`test\_set\` (8400) .

I trained several models, from on \`training set\`, \`cross-validated\` them on the \`training\_set\` only, and lastly evaluated the final model on the \`test\_set\` for generalization error.

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Now that my final model …

classification datascience dataset datasets error images mnist set test training validation