GENESIM: genetic extraction of a single, interpretable model
This work addresses the need for interpretable models in decision support applications where ensemble methods are impractical due to their lack of transparency.
The authors tackled the trade-off between interpretability and predictive performance in decision tree models by developing GENESIM, a genetic algorithm that transforms ensembles into a single, interpretable decision tree. Results on twelve datasets show GENESIM outperforms standard decision tree techniques and achieves predictive performance comparable to ensemble methods while maintaining low complexity.
Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.