Context: Prediction of dependent variables like Age, Siblings, Children, etc (which are not categorical, but bounded, and integer-valued) from a dataset using Neural Network. I'm experimenting with a simple network, and plan to train it using Gradient Descent for starters.

While RMSE is a natural choice for minimization, the predictions currently are float values. I could probably round the predictions (or ceil/floor them) and calculate the loss (again using RMSE), but is that a correct approach?

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