Training, validation, and test sets
Section of Networks in practice, part of Statistical learning
Training, validation, and test sets
| Type | Section |
| Taught in | Networks in practice |
| Part | Statistical learning |
To control generalisation the data are divided into a training set, a validation set, and a test set.
- The training set is used to optimise the weights.
- The validation set is monitored during or after training to choose when to stop, the number of nodes or weights, the learning rate, and the other tuning choices.
- The test set is used to assess how well the final model performs.
This is the same discipline as nested cross-validation. Anything chosen by looking at data is part of model building and must be assessed on data that played no part. Early stopping uses the validation error: stop at the epoch where it is smallest.
Used in
See also
Softmax is multinomial logistic regression · What has to be settled before a step · Weight initialisation · Variance argument for weight initialisation · Batch and online learning · Preparing data for a network · Output coding, softmax, and cross-entropy · Constructing and training a network