Model validation

From NorthShore Analytics
Jump to: navigation, search

Once a model has been developed, it has to be validated to ensure that it meets development specifications. Regardless of the type of the model, it has to be cross-validated on an independent data set, and the results compared with the training dataset to detect possible under- or overfitting. Specific validation methods for the types of model most commonly used by Clinical Analytics are described in the rest of this section.