Entanglement in machine learning (ML) pipelines refers to the fact that when a change is made in one of the ML pipeline steps, then other steps are affected by the change. For example, a fundamental change in the data preparation and feature engineering phases of the ML pipeline can have a drastic effect in subsequent pipeline steps, such as model fitting (training) and model evaluation/tuning.

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