Using XGBoost to Judge Cross-Universe Battles
iBoomer69 · reddit · 2026-07-15
The author built an XGBoost classification pipeline to tackle "cross-universe character battles"—a task that seems like a meme but actually serves as a stress test for models. The goal is to enable the model to make judgments under conflicting rule systems rather than simply replicating human bias.
Solution Design
- Synthetic Data Labeling: Used an LLM as a blind labeler to process 2300+ cross-domain battles. It only saw character names and their respective rule sets, not underlying stats, to force out more "native" feature weights.
- Data Leakage Check: Initial accuracy was suspiciously high, which was later traced to mirrored pair leakage in the training/test sets. After cleaning, accuracy on the clean hold-out set actually rose to 93%.
- Explainable Output: The author fed SHAP feature attributions to an LLM, applying strict domain constraints to translate numerical interpretations into user-facing natural language conclusions, rather than just outputting a win/loss result.
The project is fully deployed, with a GitHub repository and live demo available. The author emphasizes that the focus isn't on the "who wins" meme, but on how the system handles conflicting rule sets without hallucinating.
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