Random Forest Predicts Human Ratings of Creative Stories Using Very Small Training Samples

= 0.80) using as few as 25 training stories. Furthermore, RF demonstrated superior accuracy and lower reliance on story length compared to LLM-based scoring models. These findings provide a robust proof-of-concept for using simulated expert panels as a scalable alternative to (decontextualized) automated assessment methods, while reducing human raters' burden and the logistical constraints of complex rating designs. Extension of this work to different contexts, creativity tasks and domains are necessary to gauge its generalizability.