  An empirical study was carried out to compare different implementations of
ensemble models aimed at improving prediction in spectroscopic data. A wide
range of candidate models were fitted to benchmark datasets from regression and
classification settings. A statistical analysis using linear mixed model was
carried out on prediction performance criteria resulting from model fits over
random splits of the data. The results showed that the ensemble classifiers
were able to consistently outperform candidate models in our application
