Stacking, in the context of cancer research, refers to a statistical and computational technique used to improve the accuracy of predictive models. This method involves combining multiple machine learning algorithms to form a more robust model.
Due to the complexity and heterogeneity of cancer, single models often fail to capture all the nuances required for accurate prediction. Stacking allows researchers to leverage the strengths of different algorithms, leading to better predictions in areas like diagnosis, treatment planning, and prognosis.
Stacking typically involves two levels of models. The first level consists of several base models, each of which makes predictions. The second level, known as the meta-model, combines these predictions to make the final decision. This approach helps in capturing diverse patterns in the data that a single model might miss.
As artificial intelligence and machine learning continue to evolve, stacking will likely become even more integral to cancer research. Future advancements may include better algorithms for stacking, improved computational efficiency, and enhanced interpretability of stacked models.
