A Stacking-Based Ensemble Approach for Predicting Chess Puzzle Difficulty

FedCSIS 2025 competition is to predict the difficulty of chess puzzles, we present a structured multi-stage regression pipeline developed for the FedCSIS 2025 Challenge.The approach consists of three stages: (i) four Elo-banded base models trained on separate rating ranges to capture localized difficulty semantics and mitigate bias in imbalanced datasets; (ii) a feature-level stacking ensemble combining base predictions with structural attributes, such as success probabilities, failure distributions, and solution length, to enhance cross-band generalization; and (iii) a lightweight post-hoc residual correction to reduce systematic prediction biases.Additionally, an uncertaintyaware mask-based evaluation is introduced to identify the 10% most challenging puzzles for extended scoring.Our method achieved competitive results, ranking 7th in the final leaderboard, while maintaining low computational cost.These findings demonstrate that lightweight, interpretable models, when combined with structural reasoning and uncertainty estimation, can rival more complex deep-learning approaches.This study highlights the potential of structured machine learning pipelines for scalable, human-centric chess puzzle analytics.