You may want to look at what happens with test data never shown to the network or used to make decisions about its training. Pruning often improves generalization when data are abundant compared to the complexity of the problem space because you are reducing the number of parameters in the model.
You may want to look at what happens with test data never shown to the network or used to make decisions about its training. Pruning often improves generalization when data are abundant compared to the complexity of the problem space because you are reducing the number of parameters in the model.