Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm.