Some R machine learning packages present confusion matrices and performance measures during the model-building process. The purpose of these statistics is to provide insight about the model's resubstitution error, which occurs when the training data is incorrectly predicted in spite of the model being built directly from this data. This information can be used as a rough diagnostic to identify obviously poor performers.
The resubstitution error is not a very useful marker of future performance, however. For example, a model that used rote memorization to perfectly classify every training instance with zero resubstitution error would be unable to generalize its predictions to data it has never seen before. For this reason, the error rate on the training data can be extremely optimistic about a model's future performance.
Instead of relying on resubstitution error, a better practice is to evaluate a model's performance on data it has not yet seen. We used such a...
