What is the input space of a neural network (or other supervised learning algorithms)?
I guess it depends on the algorithm, but linear models, as well as neural networks, treat all variables as continuous. The algorithm will not explode or anything if you supply 0.2 at prediction stage. However, your algorithm is trained on data. The algorithm can at best do what it has learnt from the training data. For this reason, do not expect anything meaningful when you feed an example with a value that has not been seen in the whole training set, or that it does not follow the training set distribution.