| Popis: |
In this paper we propose a new approach for validation of chemometric models. It is based on k-fold cross-validation algorithm, but, in contrast to conventional cross-validation, our approach makes possible to create a new dataset, which carries sampling uncertainty estimated by the cross-validation procedure. This dataset, called pseudo-validation set, can be used similar to independent test set, giving a possibility to compute residual distances, explained variance, scores and other results, which can not be obtained in the conventional cross-validation. The paper describes theoretical details of the proposed approach and its implementation as well as presents experimental results obtained using simulated and real chemical datasets. |
