XGBoost version changes yield different results with same model and data

A user discovered that using different versions of the XGBoost library can lead to varying results, even when the model and data remain the same. This inconsistency was observed when a model trained with XGBoost 3.2.0 produced different outcomes when re-run with another version. AI

IMPACT Highlights potential reproducibility issues in machine learning workflows due to software versioning.

RANK_REASON User-reported observation about software behavior, not a formal release or research.

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