Граница принятия решения — это learned separator, который отделяет один predicted class от другого в classification problem. Форма и сложность границы зависят от model architecture, features и training process и могут быть linear или highly nonlinear.
In the case of backpropagation-based artificial neural networks or perceptrons, the type of decision boundary that the network can learn is determined by the number of hidden layers in the network. If it has no hidden layers, then it can only learn linear problems. If it has one hidden layer, then it can learn any continuous function on compact subsets of R<sup>n</sup> as shown by the Universal approximation theorem, thus it can have an arbitrary decision boundary.
The separator between classes learned by a model in a binary class or multi-class classification problems. For example, in the following image representing a binary classification problem, the decision boundary is the frontier between the orange class and the blue class: !A well-defined boundary between one class and another.