Or more broadly: how do you train a machine learning model to capture the nuances of entirely different, conflicting rule sets? I built an XGBoost classification pipeline to answer that. To stress-test the architecture across heterogeneous environments, I applied it to a highly debated and popular hypothetical: cross-universe power scaling The domain is silly. The engineering underneath it isn’t. When predicting outcomes across disparate environments, the core challenge is avoiding a lookup table of your own biases. If I manually dictate how these distinct rule sets resolve, the model just learns my heuristics. Here is how I built the architecture to prevent that: Synthetic Data Generation: I engineered an LLM to act as a blind labeler across 2,300+ cross-domain matchups. It only saw character names and their native rule sets, never the underlying stats. This forced my XGBoost classifier to derive its own feature weightings from raw, unbiased outcomes. Catching a Silent Data Leak: My initial accuracy looked suspiciously great. I audited my pipeline and caught a data leak in my train/test split that was mirroring matchups into both sets. I stripped the leak out, expecting the metric to tank. Instead, it went up—hitting 93% on a clean hold-out. The leak had actually been masking a sharper model. Explainable AI (XAI): Raw SHAP values mean nothing to an end-user. I engineered a generation layer that feeds the model’s SHAP attributions into an LLM alongside strict domain constraints. The pipeline translates its own mathematical feature importance into a plain-English, logically grounded breakdown of how the conflicting rule sets resolved. It doesn’t just output a winner; it mathematically justifies how it navigated the nuance without hallucinating. Full stack, deployed, and live. Repo: github.com/aidentejada/anime-versus-ml Live Endpoint: versus.aidentejada.com submitted by /u/iBoomer69
Originally posted by u/iBoomer69 on r/ArtificialInteligence