Does Stacking Ensembles Actually Beat Your Best Individual Machine Learning Model?
An analysis of stacking in machine learning reveals that combining models only improves performance when the constituent models make distinct errors. Understanding this principle helps data scientists determine whether complex ensemble methods are truly necessary or if a single well-tuned model is sufficient for their predictive tasks.
Source: Towards Data Science