9/16/2023 0 Comments Black box it![]() ![]() Plus, from a traditional statistics viewpoint, a neural network is a non-identifiable model: Given a dataset and network topology, there can be two neural networks with different weights but exactly the same result. Even the analysis of which input characteristic is irrelevant is a open problem (see this link). There is no simple link between the weights and the function being approximated. The black box issue is: The approximation given by the neural network will not give you any insight on the form of f. This works, and the precision can be arbitrarily small - you can expand the network, fine tune its training parameters and get more data until the precision hits your goals. Then you use the Neural Network to build an approximation of $f$ that has a error rate that is acceptable to your application. This function f can be arbitrarily complex, and might change according to the evolution of the business, so you can't derive it by hand. When you model this using a neural network, you are supposing that there is a function $f(C)=R$, in the proper sense of a mathematical function. You have a matrix of input characteristics $C$ (sex, age, income, etc) and a vector of results $R$ ("defaulted", "not defaulted", etc). ![]() A neural network is a black box in the sense that while it can approximate any function, studying its structure won't give you any insights on the structure of the function being approximated.Īs an example, one common use of neural networks on the banking business is to classify loaners on "good payers" and "bad payers". ![]()
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