Neural network with missing features in prediction data? Lets say I want to create a model that predicts an outcome of a certain match. I use features to train the network that are known before a match starts (such as individual performance of each player in the last x months, etc.) and some features that are not known beforehand (just data about how the match went, when certain things happened, etc).
Now, if I want to predict a result of an upcoming match (with some features missing, since they are not know before the match starts). Will my NN perform worse since I used features that I don't have at the time of the prediction to train it or would it perform the same/better compared to a model based on only features that are know beforehand.
And if it's alright to have missing data in the prediction set, how do I go about marking certain features as "missing". Do I simply enter a 0 in those columns?

Your training (and validation) should mimic the prediction environment. That said, the features not available to you at prediction time shouldn't be used at training time. This is the source of your confusion.
