Building an XGBoost Pipeline for Cross-Domain Conflict Resolution with Explainable AI

Short summary

An ML engineer built an XGBoost classification pipeline to model outcomes across conflicting rule sets, using a hypothetical cross-universe power-scaling domain as a stress test. The architecture uses an LLM as a blind labeler over 2,300+ matchups, SHAP values fed back into an LLM for plain-English explanations, and caught a silent data leak that was actually masking a sharper model. The project is deployed live with a public repo.

• •XGBoost pipeline for cross-domain conflict resolution with 93% hold-out accuracy
• •LLM used as blind synthetic labeler; SHAP values translated to plain-English explanations
• •Caught and fixed a data leak that was masking model performance; full repo and live endpoint shared

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