The criteria is a loss function, which can be whatever works best for the situation. Some might have statistical interpretations, but it’s not really a necessity. For Boolean true/false there are many to choose from. Hinge loss and logistic loss are two common ones. The former is the basis for support vector machines.
But the choice of loss is just one small part in the design of a deep learning model. Choice of activation functions, layer connectivity, regularization and optimizer must also be considered. Not all of these have statistical interpretations. Like, what is the statistical interpretation between the choice of Relu and Leaky Relu? People seemed to prefer one over the other because that’s what worked best for them.
The criteria is a loss function, which can be whatever works best for the situation. Some might have statistical interpretations, but it’s not really a necessity. For Boolean true/false there are many to choose from. Hinge loss and logistic loss are two common ones. The former is the basis for support vector machines.
But the choice of loss is just one small part in the design of a deep learning model. Choice of activation functions, layer connectivity, regularization and optimizer must also be considered. Not all of these have statistical interpretations. Like, what is the statistical interpretation between the choice of Relu and Leaky Relu? People seemed to prefer one over the other because that’s what worked best for them.