Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success
Predicting startup success requires models that are both accurate and interpretable. We present a lightweight ensemble framework that combines YES/NO questions generated by large language models (LLMs), forming a transparent decision-making system. Each question acts as a weak heuristic, and by filtering, ranking, and aggregating them through a threshold-based voting mechanism, we construct a strong ensemble predictor. On a test set where 10% of startups are classified as successful, our approach achieves a precision rate of 50%, representing a 5x improvement over random selection, while remaining fully transparent. When we incorporate expert-guided heuristics into the generation process, performance improves further to 54% precision. These results highlight the value of combining LLM reasoning with human insight and demonstrate that simple, interpretable ensembles can support high-stakes decisions in domains such as venture capital (VC).
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Interpreting Tree Ensembles with inTrees
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and…
Future predictionGeneral ClassificationregressionLionForests: Local Interpretation of Random Forests
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performan…
BIG-bench Machine LearningMaking Tree Ensembles Interpretable
Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that i…
Generating Compact Tree Ensembles via Annealing
Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on ver…
Machine Learning with Probabilistic Law Discovery: A Concise Introduction
Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it diffe…
Anomaly DetectionTime SeriesTime Series Analysis