When this training process is complete, the values of the weights are fixed. At this point these weights have essentially encoded the intelligence of the training set into the neural network. The neural network is then able to predict further outputs on the basis of information supplied as inputs along with the weights that were determined in the training process. The reliability of the prediction is determined by quality of the training data, the structure of the neural network and the success of the training process. An accurate prediction requires that the input values must be similar to some values found in the training set.
Neural networks are poor at extrapolating far outside the training set.
Such is the learning potential of a neural network that correlations with random inputs and outputs may be good. However, if random data is used to train the network, the network predictions will likewise be random and useless. The network is a black box inasmuch as it provides no information to indicate the rationale for the linking of input and output data. The values of the weights have no actual relevance in interpreting any causal relationships in the data. Return to Top Where to Go for More Information Neural networks are highly versatile tools which are used in a wide variety of disciplines to predict some performance attributes from known data.