Dynamic Model Switching for Improved Accuracy in Machine Learning

arXiv:2404.18932v1 Announce Type: new
Abstract: In the dynamic landscape of machine learning, where datasets vary widely in size and complexity, selecting the most effective model poses a significant challenge. Rather than fixating on a single model, our research propels the field forward with a novel emphasis on dynamic model switching. This paradigm shift allows us to harness the inherent strengths of different models based on the evolving size of the dataset.
Consider the scenario where CatBoost demonstrates exceptional efficacy in …

abstract accuracy arxiv challenge complexity cs.lg datasets dynamic landscape machine machine learning novel research type