2.4.17 | Confidence analysis of a neural network Supervised neural networks that use an MSE cost function can use formal
statistical methods to determine the confidence of the trained model. The MSE on
a validation set can be used as an estimate for variance. This value can then be
used to calculate the confidence interval of the output of the network, assuming a
normal distribution. A confidence analysis made this way is statistically valid as
long as the output probability distribution stays the same and the network is not
modified.
By assigning a softmax activation function on the output layer of the neural
network (or a softmax component in a component-based neural network) for
categorical target variables, the outputs can be interpreted as posterior
probabilities. This is very useful in classification as it gives a certainty measure on
classifications.
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