Machine learning experiments are rarely deterministic. Models are trained with random splits, random initialization, stochastic optimization, and often on GPUs that are not strictly deterministic. This means that rerunning the same experiment can produce different numbers. Reproducibility in machine learning is therefore not about getting the exact same result every time. It is about designing experiments so that the conclusions remain stable despite randomness.
