  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
handling smaller datasets, providing nuanced insights and accurate predictions.
However, as datasets grow in size and intricacy, XGBoost, with its scalability
and robustness, becomes the preferred choice.
  Our approach introduces an adaptive ensemble that intuitively transitions
between CatBoost and XGBoost. This seamless switching is not arbitrary;
instead, it's guided by a user-defined accuracy threshold, ensuring a
meticulous balance between model sophistication and data requirements. The user
sets a benchmark, say 80% accuracy, prompting the system to dynamically shift
to the new model only if it guarantees improved performance.
  This dynamic model-switching mechanism aligns with the evolving nature of
data in real-world scenarios. It offers practitioners a flexible and efficient
solution, catering to diverse dataset sizes and optimising predictive accuracy
at every juncture. Our research, therefore, stands at the forefront of
innovation, redefining how machine learning models adapt and excel in the face
of varying dataset dynamics.
