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Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". [3] A model should be built for a specific purpose or set of objectives and its validity determined for that purpose. [3]
A model can be validated only relative to some application area. [2] [3] A model that is valid for one application might be invalid for some other applications. As an example, consider the curve in Figure 1: if the application only used inputs from the interval [0, 2], then the curve might well be an acceptable model.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Validations can be broken into three stages: validating the raw data collected (data validation), the interpretation of the data into the atomic model (model-to-data validation), and finally validation on the model itself. While the first two steps are specific to the technique used, validating the arrangement of atoms in the final model is not.
Figure 2: OPQ Validation Process (adapted from the typical V-Model) This combined testing of OQ and PQ phases is sanctioned by the European Commission Enterprise Directorate-General within ‘Annex 15 to the EU Guide to Good Manufacturing Practice guide’ (2001, p.