Welke, Pascal and Alkhoury, Fouad and Bauckhage, Christian and Wrobel, Stefan (2021) Decision Snippet Features. In: 2020 25th International Conference on Pattern Recognition (ICPR) :. IEEE, pp. 4260-4267. ISBN 9781728188096
Full text not available from this repository.Abstract
Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees - random forests - are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.