Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
This addresses the problem of interpretability versus accuracy for users of random forests, offering a method to simplify models, though it appears incremental.
The study tackled the trade-off between model complexity and accuracy in random forests by decomposing trees into classification rules and selecting subsets, finding that a few rules can achieve accuracy close to the original model and sometimes outperform it.
We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them. We show experimentally that already a few rules are sufficient to achieve an acceptable accuracy close to that of the original model. Moreover, our results indicate that in many cases, this can lead to simpler models that clearly outperform the original ones.