Overfitting Sample Clauses

Overfitting. When data is initially presented to an XXX at the start of training, the error within the network is large, with a high degree of variance from the data solution, to which the XXX gradually adapts to reduce error over time. Without appropriate monitoring, this error function can be reduced to such a low level, that the data has completely learned the set of data provided for training. This will lead to extremely impressive results if the XXX is run on the training set, as it will have remembered each pattern of data previously presented to it. Unfortunately, if new data is presented then the XXX will have a sudden loss in predictive accuracy as it will not fit into previously learned data. There are multiple strategies to prevent overfitting – which can be broadly divided into iterative training, reduced variable selection and early stopping. Iterative training is a group of collective strategies whereby the network is trained multiple times on either the same set of data or variously divided subsets with hold out of some of the data as a validation set. Reduced variable selection, or pruning, aims to reduce the complexity of the network on sequential training runs to produce an XXX with the fewest number of input variables, whilst maintaining output accuracy. Early stopping of training puts a cap on the number of iterations a network can run without any reduction in the error function of the XXX, typically 100 iterations. These strategies combined can produce a simpler robust network whilst improving generalizability.